TY - CONF
A1 - Naik, Aakash
A1 - Ertural, Christina
A1 - Dhamrait, Nidal
A1 - Benner, Philipp
A1 - George, Janine
T1 - A Quantum-Chemical Bonding Database for Solid-State Materials
N2 - Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from
bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information.
T2 - 16th International conference on materials chemistry (MC16)
CY - Dublin, Ireland
DA - 03.07.2023
KW - Bonding analysis
KW - Automation
KW - Materials Informatics
KW - Computational Chemistry
KW - Database
PY - 2023
AN - OPUS4-57889
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Understanding and Machine Learning of Materials Properties with Quantum-Chemical Bonding Analysis
N2 - Bonds and local atomic environments are crucial descriptors for material properties. They have been used to create design rules for materials and are used as features in machine learning of material properties. This talk will show how our recently developed tools, that automatically perform quantum chemical bond analysis and enable the study of chemical bonds and local atomic environments, accelerate and improve the development of such heuristics and machine-learned models for materials properties.
T2 - Accelerate Conference 2023
CY - Toronto, Canada
DA - 22.08.2023
KW - Automation
KW - DFT
KW - Bonds
PY - 2023
AN - OPUS4-58131
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - George, Janine
T1 - Handling of orbital-resolved "ICOHPLIST.lobster" files from the software Lobster in pymatgen
N2 - Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. This open-source software package powers the Materials Project.
In this particular contribution, the handling of obital-resolved "ICOHPLIST.lobster" files from Lobster was implemented in the software package (github handle: @JaGeo).
KW - Bonding analysis
KW - Density functional theory
PY - 2023
UR - https://github.com/materialsproject/pymatgen/pull/2993
PB - Zenodo
CY - Geneva
AN - OPUS4-57569
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Naik, Aakash
A1 - Ertural, Christina
A1 - Dhamrait, Nidal
A1 - Benner, Philipp
A1 - George, Janine
T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 5)
N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials."
Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID)
KW - Bonding analysis
KW - Automation
KW - Materials Informatics
KW - Computational Chemistry
KW - Database
PY - 2023
DO - https://doi.org/10.5281/zenodo.7852806
PB - Zenodo
CY - Geneva
AN - OPUS4-57445
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Naik, Aakash
A1 - Ertural, Christina
A1 - Dhamrait, Nidal
A1 - Benner, Philipp
A1 - George, Janine
T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 6)
N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials."
Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID)
KW - Bonding analysis
KW - Automation
KW - Materials Informatics
KW - Computational Chemistry
KW - Database
PY - 2023
DO - https://doi.org/10.5281/zenodo.7852808
PB - Zenodo
CY - Geneva
AN - OPUS4-57446
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Naik, Aakash
A1 - Ertural, Christina
A1 - Dhamrait, Nidal
A1 - Benner, Philipp
A1 - George, Janine
T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 7)
N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials."
Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID)
KW - Bonding analysis
KW - Automation
KW - Materials Informatics
KW - Computational Chemistry
KW - Database
PY - 2023
DO - https://doi.org/10.5281/zenodo.7852820
PB - Zenodo
CY - Geneva
AN - OPUS4-57447
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Bustamante, Joana
A1 - Naik, Aakash
A1 - Ueltzen, Katharina
A1 - Ertural, Christina
A1 - George, Janine
T1 - Thermodynamic and Thermoelectric Properties of the Canfieldite, (Ag8SnS6 ), in the Quasi-Harmonic Approximation
N2 - Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties. One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, Slade group found a new low-temperature (LT) phase transition of canfieldite at 120K. Therefore, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density-functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Lattice parameters were overestimated by 2%, and thermal properties such as the constant-pressure heat capacity Cp are very close to experimental measurements. Our simulations also reveal a possible new phase transition at around 312 K. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with some argyrodites analogues, Ag8TS6 (T = Si, Ge, Ti and Sn), finding a relationship between the anharmonicity and low thermal conductivity.
T2 - TDEP Summer School 2023 (TDEP2023: Finite-temperature and anharmonic response properties of solids in theory and practice)
CY - Linköping, Sweden
DA - 20.08.2023
KW - Thermoelectric materials
KW - DFT
KW - QHA
KW - Grüneisen parameter
PY - 2023
AN - OPUS4-58147
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Automated quantum-chemical bonding analysis with workflow tools
N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, other recent workflow contributions to the Materials Project software infrastructure (pymatgen, atomate2) related to phonons and machine-learning potentials will be discussed.
T2 - ADIS Workshop 2023
CY - Tegernsee, Germany
DA - 29.10.2023
KW - Automation
KW - Materials Properties
KW - DFT
KW - Workflows
KW - Machine Learning
KW - Thermodynamic Properties
PY - 2023
AN - OPUS4-58745
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Bustamante Pineda, Joana Cecibel
A1 - George, Janine
T1 - Sulfide-Argyrodites: Thermal properties within the QHA
N2 - Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties. One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, experimentalists have found a new low-temperature (LT) phase transition of canfieldite at 120K. Therefore, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density-functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Lattice parameters were overestimated by 2%, and thermal properties such as the constant-pressure heat capacity Cp are very close to experimental measurements. Our simulations also reveal a possible new phase transition at around 312 K. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with some argyrodites analogues, Ag8TQ6 (T = Si, Ge, Ti and Sn; Q = S, Se), finding a relationship between the anharmonicity and low thermal conductivity.
T2 - SALSA Make and Measure Conference
CY - Berlin, Germany
DA - 13.09.2023
KW - Thermoelectric materials
KW - DFT
KW - QHA
KW - Grüneisen parameter
PY - 2023
AN - OPUS4-58368
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Data-driven chemical understanding with bonding analysis
N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials.
T2 - 2023 MRS Fall Meeting & Exhibit
CY - Boston, Massachusetts, USA
DA - 26.11.2023
KW - Automation
KW - Machine learning
KW - Materials Understanding
KW - Magnetism
KW - Phonons
PY - 2023
AN - OPUS4-59002
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Data-Driven Chemical Understanding with Bonding Analysis
N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
T2 - Otaniemi Center for Atomic-scale Materials Modeling Seminar
CY - Aalto, Finland
DA - 11.03.2024
KW - Automation
KW - Materials Design
KW - Bonding Analysis
KW - Machine Learning
PY - 2024
AN - OPUS4-59671
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Data-driven approaches for materials understanding and design
N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.
T2 - Machine Learning of First Principles Observables
CY - Berlin, Germany
DA - 08.07.2024
KW - Automation
KW - Bonding Analysis
KW - Machine Learned Potentials
PY - 2024
AN - OPUS4-60660
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - New Opportunities for Data-Driven Chemistry and Materials Science Through Automation
N2 - In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based). This ranges from the automation of simple heuristics (oxidation states, coordination environments) to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities. For example, we can now easily make data-driven tests of well-known rules and heuristics or develop quantum chemistry-based materials descriptors for machine learning approaches. These tests and descriptors can have applications related to magnetic ground state predictions of materials relevant for spintronic applications or for predicting thermal properties relevant for thermal management in electronics. Combining high-throughput ab initio computations with fitting, fine-tuning machine learning models and predictions of such models within complex workflows is also possible and promises further acceleration in the field. In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science.
T2 - MRS Spring
CY - Seattle, Washington, USA
DA - 21.04.2024
KW - Automation
KW - Workflows
KW - Chemical Bonding
KW - Thermal Properties
KW - Materials Design
PY - 2024
AN - OPUS4-59982
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - High-throughput and automated bonding Analysis
N2 - Talk as a part of the LOBSTER CECAM SCHOOL. This talk introduced all participants to automation tools around the software LOBSTER.
T2 - CECAM LOBSTER School
CY - Aalto, Finland
DA - 12.03.2024
KW - Automation
KW - Workflows
KW - Bonding Analysis
KW - Materials Design
KW - Chemically Complex Materials
PY - 2024
AN - OPUS4-59672
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Data-driven chemical understanding with bonding analysis
N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
T2 - Seminar of the Department of Chemistry at Imperial College London
CY - Online meeting
DA - 20.02.2024
KW - Automation
KW - Magnetism
KW - Bonding Analysis
KW - Machine Learning
PY - 2024
AN - OPUS4-59546
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Ueltzen, Katharina
A1 - George, Janine
T1 - Bonding analysis results for "Chemical ordering and magnetism in face-centered cubic CrCoNi alloy"
N2 - This repository contains the code and data to produce the results of chapter IIIC. Covalent bonding analysis for L12/L10 type configurations of the publication Chemical ordering and magnetism in face-centered cubic CrCoNi alloy by Sheuly Ghosh et al.
KW - Magnetism
KW - Bonding Analysis
KW - Medium Entropy Alloys
PY - 2024
DO - https://doi.org/10.5281/zenodo.11104874
PB - Zenodo
CY - Geneva
AN - OPUS4-59987
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - High-throughput Approaches for Materials Understanding and Design
N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties.[1] Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available.[2,3] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale.[3,4] The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.[7–10]
T2 - International Materials Science and Engineering Congress - MSE 2024
CY - Darmstadt, Germany
DA - 24.09.2024
KW - Automation
KW - High-throughput
KW - Chemically Complex Materials
KW - Thermal Properties
KW - Phonons
KW - Bonding Analysis
PY - 2024
AN - OPUS4-61118
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - High-throughput approaches for materials understanding and design
N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties.[1] Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available.[2,3] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale.[3,4] The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.[7–11]
T2 - TCO 2024 - Transparent Conductive Oxides, Fundamentals and Applications
CY - Leipzig, Germany
DA - 23.09.2024
KW - Automation
KW - Materials discovery
KW - Machine Learned Interatomic Potentials
KW - Workflows
KW - Chemically Complex Materials
KW - Bonding Analysis
PY - 2024
AN - OPUS4-61153
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - High-Throughput Approaches for Materials Understanding and Design
N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.
T2 - International Symposium on Computational Structure Prediction and Advanced Materials
CY - Louvain-la-Neuve, Belgium
DA - 22.08.2024
KW - Automation
KW - High-throughput
KW - Magnetism
KW - Phonons
KW - Machine learning
KW - Chemically complex materials
PY - 2024
AN - OPUS4-60870
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - New opportunities through an efficient combination of machine learning and high-throughput computing
N2 - The underlying data is crucial for machine learning (ML) tasks.[1] Ab initio data is often used as a target (occasionally as features[2]). High-throughput calculations and automation make it possible to generate such data as efficiently as possible and with uniform standards.[3,4] The high-throughput data of the Materials Project has recently been used to train new foundation interatomic potentials[5].
Several high-throughput frameworks have been developed in recent years. This presentation will introduce the atomate2/jobflow[3,4] framework that is used and developed by researchers around the Materials Project. In the future, it will be used to generate the data for the Materials Project.
Atomate2 allows both the use of DFT and ML potentials within one framework. Building upon this framework, we have developed a package with the possibility to train and benchmark ML potentials automatically. Currently, the package can be used to train ML interatomic potentials sufficient to predict harmonic phononic properties of materials. Typical foundation models in this area now achieve acceptable results but are still far from routinely replacing DFT.[5,6] Building on the promising results from the ref where we explored data generation strategies for accurate phononic properties[7], we will present new fully automated workflows for training and benchmarking ML interatomic potentials with force predictions that are accurate enough to compute harmonic phonons in very good agreement with DFT for different crystal structures of the same composition.
T2 - GAP/(M)ACE Developers & Users Meeting 2024
CY - Berlin, Germany
DA - 17.09.2024
KW - Automation
KW - Workflows
KW - Machine Learned Interatomic Potentials
KW - Thermal Properties
KW - Bonding Analysis
KW - Chemically Complex Materials
PY - 2024
AN - OPUS4-61099
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Data-Driven Approaches for Materials Understanding and Design
N2 - Data-Driven Approaches for Materials Understanding and Design
Analyzing the crystal structure (including coordination environments, bond distances, and angles) and chemical bonding are standardly used in chemistry to understand material properties. In this talk, I will show how we can use this information in machine learning of material properties. For this purpose, I will present our software tools ChemEnv[1] for analyzing coordination environments and for automation of orbital-based bonding analysis with LOBSTER[2] (LobsterPy[3] and density-functional theory workflows in atomate2[4]). Enabled by these software tools, we have built interpretable machine-learned models for magnetic and vibrational properties that allow us to test and further develop intuitive rules.[5,6]
T2 - Institutskolloqium Institut für Anorganische Chemie und Kristallographie
CY - Leipzig, Germany
DA - 23.10.2024
KW - Automation
KW - Bonding Analysis
KW - Materials Design
KW - Chemically Complex Materials
KW - Phonons
KW - Thermal Properties
KW - Synthesizability
KW - Machine Learning
PY - 2024
AN - OPUS4-61445
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - George, Janine
ED - Bastian, P.
ED - Dranzlmüller, D.
ED - Brüchle, H.
ED - Mathias, G.
T1 - Accelerated Materials Discovery with Automation and MachineLearned Chemical Knowledge
N2 - This project aims to accelerate the search for new materials (e.g., for thermoelectric applications, battery materials, magnets, and other materials classes) based on ab initio highthroughput studies. Highthroughput searches are typically restricted to known materials. This project explores strategies (datadriven chemical heuristics in subproject 1 and machinelearned interatomic potentials in subproject 2) to go beyond current database entries and include such computationally demanding properties in highthroughput searches. To accomplish each subproject, we develop automated workflows for highthroughput computations and provide large open databases of computed materials properties to the research community.
KW - Automation
KW - Chemically Complex Materials
KW - Machine Learning
KW - Machine-Learned Interatomic Potentials
KW - Workflows
PY - 2024
UR - https://doku.lrz.de/files/10745976/1136429625/1/1731329162337/2024_SuperMUC-Results-Reports.pdf
SN - 978-3-9816675-6-1
SP - 72
EP - 75
AN - OPUS4-61618
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - High-throughput and data-driven approaches for materials understanding and design
N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties.[1] Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available.[2,3] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale.[3,4] The lecture will demonstrate how our tools, which assess local atomic environments and perform automatic bonding analysis, help to develop new machine-learning models and a new intuitive understanding of materials.[5,6]
Beyond this, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.[7–11] The focus will be especially on the interplay of DFT and machine-learned interatomic potentials will present new fully automated workflows for training and benchmarking machine-learned interatomic potentials with force predictions accurate enough to compute harmonic phonons in very good agreement with DFT. Semi-automated fine-tuning of existing ML models for phonon properties will also be discussed.
T2 - XXIV Chilean Physics Symposium
CY - Temucu, Chile
DA - 20.11.2024
KW - Automation
KW - Machine Learning
KW - Chemically Complex Materials
KW - Machine-Learned Interatomic Potentials
KW - Finetuning
PY - 2024
AN - OPUS4-61749
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - George, Janine
T1 - Materialinformatik
N2 - Die Materialinformatik ermöglicht es, vorhandenes/etabliertes chemisches Wissen zu überprüfen, aber auch neues Wissen zu generieren. Zu diesem Zweck wurden automatisierte Methoden entwickelt, die es ermöglichen, große Datenbanken mit Materialeigenschaften zu berechnen. Diese Datenbanken mit berechneten Eigenschaften können nun mit Datenanalysetechniken und maschinellem Lernen ausgewertet werden. Unsere Entwicklungen ermöglichen es insbesondere, die Analyse der chemischen Bindung mit anderen Materialeigenschaften in großem Maßstab zu kombinieren. Auf der Grundlage von Deskriptoren für chemische Bindungen haben wir die ersten Schritte zur Entwicklung neuer chemischer Regeln unternommen. Daneben entwickeln wir weitere Programmcodes, die die Tätigkeiten in der Materialinformatik deutlich erleichtern
KW - Materialdesign
KW - Maschinelles Lernen
KW - Automatisierung
KW - Hochdurchsatz
KW - Workflows
KW - Materialeigenschaften
PY - 2024
SN - 978-3-8007-6472-3
SP - 14
EP - 32
CY - Berlin
AN - OPUS4-62256
LA - deu
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Neue Wege in der Materialforschung: Zusammenspiel von Hochdurchsatz-Simulationen und maschinellem Lernen
N2 - Dies ist meine Antrittsvorlesung an der Friedrich-Schiller-Universität Jena, die im Rahmen der BAM-Universität-Jena-Kooperation entstanden ist. Hier stelle ich die Materialinformatik und unsere Forschung im speziellen vor.
T2 - Antrittsvorlesung an der Friedrich-Schiller-Universität Jena
CY - Jena, Germany
DA - 20.01.2025
KW - Automation
KW - Ab initio
KW - Machine learning
KW - Phonons
KW - Materialdesign
KW - Materials Acceleration Platforms
PY - 2025
AN - OPUS4-62437
LA - deu
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Materials design using chemical heuristics, workflows, and machine learning
N2 - Bonds and local atomic environments are key descriptors of material properties, used to establish design rules and heuristics, and serve as descriptors in machine-learned interatomic potentials and the general machine learning of material properties.
Software implementations such as ChemEnv and LobsterEnv identify local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis (here using Crystal Orbital Hamilton Populations as computed with LOBSTER). Fully automated workflows and analysis tools now enable large-scale quantum-chemical bonding analysis. The first part of the lecture will demonstrate how these tools help develop new machine-learning models and intuitive understandings of material properties.
New universal machine-learned interatomic potentials, such as MACE-MP-0, have been developed. The second part of the lecture will showcase how these potentials, combined with DFT, can accelerate research. It will focus on the interplay between DFT and machine-learned interatomic potentials, presenting automated workflows for training, fine-tuning, and benchmarking these potentials, implemented in our software autoplex. Additionally, it will show how to train new interatomic potentials from scratch by exploring potential energy surfaces, with the potential to enhance current universal machine-learned potentials.
The lecture will also discuss the trend toward automation in computational materials science and our recent contributions.
T2 - FAIRmat Seminar
CY - Berlin, Germany
DA - 05.06.2025
KW - Automation
KW - Machine learning
KW - Synthesizability
KW - Sustainable materials design
KW - High-throughput
KW - Bonding analysis
KW - Materials acceleration platforms
PY - 2025
AN - OPUS4-63315
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Automated Bonding Analysis with LobSTER and ATOMATE2
N2 - The talk introduced automated bonding analysis with the programs LobsterPy and atomate2. I especially emphasized how bonding descriptors can be used for materials design.
T2 - Lobster Bonding Analysis School
CY - Berlin, Germany
DA - 11.06.2025
KW - Automation
KW - Bonding Analysis
KW - Materials Design
KW - High throughput
KW - Ab initio
KW - Thermal conductivity
PY - 2025
AN - OPUS4-63420
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Lobster workflow and applications
N2 - This talk introduced bonding analysis to the participants of the school including several examples of its usefulness. Additionally, I talked in detail about the workflows related to bonding analysis within the atomate2 workflow library. I then showed how the workflow was applied to build a very large bonding data database that can now be used for machine learning of materials properties.
T2 - "Automated ab initio workflows with Jobflow and Atomate2" CECAM Flagship school
CY - Lausanne, Switzerland
DA - 17.03.2025
KW - Automation
KW - Materials design
KW - Machine learning
KW - Thermal conductivity
KW - Inorganic materials
KW - High-throughput
PY - 2025
AN - OPUS4-62948
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Phonon workflow and applications
N2 - This talk introduced the participants to phonons and how they are typically computed. Then, I introduced the participants to the harmonic phonon, the Grüneisen, and the quasi-harmonic workflow that allows the computation of properties related to phonons. I also had detailed examples of how these workflows can be used in practice. Beyond this, I showed how these workflows can be used to automatically benchmark interatomic potentials and use them for the development of such potentials.
T2 - "Automated ab initio workflows with Jobflow and Atomate2" CECAM Flagship school
CY - Lausanne, Switzerland
DA - 17.03.2025
KW - Automation
KW - Phonons
KW - Thermal Conductivity
KW - Machine Learning
KW - Software Development
KW - Machine Learned interatomic Potentials
PY - 2025
AN - OPUS4-62949
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Data-Driven Materials Design
N2 - Implementations and algorithms, such as ChemEnv and LobsterEnv, for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are now available. Fully automatic workflows and analysis tools have been developed to use quantumchemical bonding analysis on a large scale. The first part of the lecture will demonstrate how our tools, which assess local atomic environments and perform automatic bonding analysis, help develop new machine-learning models and a new intuitive understanding of material properties. Many new universal machine-learned interatomic potentials, such as MACE-MP-0, have been developed. The second part of the lecture will showcase how these potentials, in combination with DFT, could significantly accelerate our research. The focus will be on the interplay of DFT and machine-learned interatomic potentials, presenting new fully automated workflows for training, finetuning, and implemented benchmarking these potentials, in our software autoplex (https://github.com/autoatml/autoplex). Additionally, I will show how to train new interatomic potentials from scratch by exploring potential energy surfaces extensively, offering a method to enhance current universal machine-learned potentials. Beyond this, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.
T2 - AI4AM2025
CY - Donostia, Spain
DA - 08.04.2025
KW - Automation
KW - Machine Learning
KW - Chemically Complex Materials
KW - Thermal Conductivity
KW - Bonding Analysis
KW - Workflows
KW - Machine Learned Interatomic Potentials
PY - 2025
AN - OPUS4-62950
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - INPR
A1 - Liu, Yuanbin
A1 - Zhou, Yuxing
A1 - Ademuwagun, Richard
A1 - Walterbos, Luc
A1 - George, Janine
A1 - Elliott, Stephen R.
A1 - Deringer, Volker L.
T1 - Medium-range structural order in amorphous arsenic
N2 - Medium-range order (MRO) is a key structural feature of amorphous materials, but ist origin and nature remain elusive. Here, we reveal the MRO in amorphous arsenic (a-As) using advanced atomistic simulations, based on machine-learned potentials derived using automated workflows. Our simulations accurately reproduce the experimental structurefactor of a-As, especially the first sharp diffraction peak (FSDP), which is a signature of MRO. We compare and contrast the structure of a-As with that of its lighter homologue, red amorphous phosphorus (a-P), identifying the dihedral-angle distribution as a key factor differentiating the MRO in both. The pressure-dependent structural behaviors of a-As and a-P differ as well, which we link to the interplay of ring topology and structural entropy. We finally show that the origin of the FSDP is closely correlated with the size and spatial distribution of voids in the amorphous networks. Our work provides fundamental insights into MRO in an amorphous elemental system, and more widely it illustrates the usefulness of automation for machine-learning-driven atomistic simulations.
KW - Materials Design
KW - Amorphous
KW - Machine Learning
KW - Machine Learning Interatomic Potentials
KW - Automation
PY - 2025
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-640204
DO - https://doi.org/10.48550/arXiv.2509.02484
SP - 1
EP - 43
AN - OPUS4-64020
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design
N2 - Despite significant progress, computational materials design still faces challenges, especially when simulating large systems needed to describe defects, interfaces, or amorphous states with the accuracy of density functional theory (DFT) or beyond.[1] To address these limitations, machine learning (ML) methods have become increasingly popular in recent years. In this talk, I will present how we’ve developed robust data generation strategies that support the creation and benchmarking of new ML models.[2] I’ll focus on methods for large-scale quantum-chemical bonding analysis and workflows for ML interatomic potentials. We’ve shown that quantum-chemical bonding properties can be used in ML models to predict phononic properties.[3] This enables us to validate several expected correlations— such as the link between bonding strength and force constants—on a large scale. Additionally, we’ve built an automated training framework for machine-learned interatomic potentials (autoplex).[4] Initial workflows include random structure searches, which are well-suited for general-purpose potentials, as well as workflows tailored to ML potentials with accurate phonon properties. While atomistic simulations are highly effective for certain material properties, others— like magnetism or synthesizability—remain difficult. In these cases, it’s promising to benchmark established ab initio methods against chemical heuristics or to develop new ML models based primarily on experimental data.[5,6]
T2 - SusML (Sustainable Machine Learning Workshop)
CY - Dresden, Germany
DA - 29.09.2025
KW - Materials Design
KW - Materials Acceleration Platforms
KW - Automation
KW - Workflows
KW - Machine Learned Interatomic Potentials
KW - Synthesizability
KW - Magnetism
PY - 2025
AN - OPUS4-64244
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Bustamante, Joana
A1 - Naik, Aakash
A1 - Ueltzen, Katharina
A1 - George, Janine
A1 - Ertural, Christina
T1 - Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study. DFT-part
N2 - This repository contains computational data supporting the manuscript titled *“Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study”* It includes raw data for vibrational properties, elastic properties and Bonding analysis.
KW - DFT
KW - QHA
KW - Lattice thermal conductivity
KW - Grüneisen parameter
PY - 2025
DO - https://doi.org/10.5281/zenodo.17399975
PB - Zenodo
CY - Geneva
AN - OPUS4-64671
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Bustamante, Joana
A1 - Naik, Aakash
A1 - Ueltzen, Katharina
A1 - Ertural, Christina
A1 - George, Janine
T1 - Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study.
N2 - This repository includes raw data for bonding analysis and lattice thermal conductivity using MLIP-MACE-MP03b, supporting the manuscript “Thermal Transport in Ag8TS6 (T= Si, Ge, Sn) Argyrodites: An Integrated Experimental, Quantum-Chemical, and Computational Modelling Study”
KW - DFT
KW - LOBSTER
KW - Lattice thermal conductivity
KW - MLIP
PY - 2025
DO - https://doi.org/10.5281/zenodo.17397456
PB - Zenodo
CY - Geneva
AN - OPUS4-64674
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Namrata, Jaykhedkara
A1 - George, Janine
T1 - Atomistic interaction at the solid interface between Li6PS5Cl and Li metal
N2 - Solid-state batteries offer higher energy density and improved safety than conventional lithium-ion cells with flammable liquid electrolytes, [1] but poor interfacial compatibility at the solid-electrolyte (SE)|electrode interface, especially with lithium metal anodes, remains a major challenge. [2] To gain a deeper understanding of the structural and chemical factors governing the formation and growth of this interface, we selected Li6PS5Cl (SE) and Li metal anode as prototype materials. In this work, we begin by performing ab initio molecular dynamics accelerated by machine-learning potentials in VASP, to obtain lattice parameters and bulk moduli of the bulk Li6PS5Cl and Li metal phases, thereby quantifying lattice-mismatch strain, compressibility differences, and their thermal evolution. We then analyze the atomic structure and coordination environments at the Li6PS5Cl|Li interface, which is carefully constructed for the [100] orientation for both SE and Li, allowing the lowest possible strain (see Fig. 1). Dominant bonding motifs are identified using Crystal Orbital Hamilton Population [3] analysis via LOBSTER.[4] Radial distribution functions of the interface are compared with those of the respective bulk phases in the 200-400 K range to elucidate temperature-driven structural rearrangements. This combined analysis reveals how interfacial bonding evolves with temperature and provides critical insight into chemical and mechanical stability at the interface. Our findings offer a quantitative framework for correlating bulk properties with interfacial structure, thereby informing the design of more robust SEs and engineering strategies to improve interfacial compatibility.
T2 - 12th Workshop “Lithium-Sulfur Batteries”
CY - Dresden, Germany
DA - 17.11.2025
KW - Solid-state batteries
KW - Interface chemistry
KW - Machine learning-molecuar dynamics
PY - 2025
AN - OPUS4-64804
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Robust data generation, heuristics and machine learning for designing sustainable materials
N2 - Despite advances in computational materials design, simulating large systems—such as defects, interfaces, or amorphous states—with quantum-chemical accuracy remains a major challenge.[1] Machine learning (ML) methods are emerging as powerful tools to overcome these limitations, enabling scalable and accurate modeling beyond traditional quantum-chemical approaches.[2] They also open new avenues for discovering non-toxic, earth-abundant alternatives to existing materials and can be combined with self-driving labs. [3] There are nowadays robust data generation strategies that underpin the development and benchmarking of ML models. [4,5]atomate2 I will focus on such strategies for quantum-chemical bonding analysis and ML interatomic potentials in my talk. Quantum-chemical bonding descriptors can be effectively used in ML models to predict phononic properties. [6] ML interatomic potentials offer a powerful approach for predicting energies, forces, and stresses—but their performance hinges on high-quality training data. Our automated framework, autoplex, enables diverse and scalable training workflows, from random structure searches for general-purpose models to phonon-aware pipelines for high-accuracy predictions.[7] While quantum chemistry excels in many domains, properties like magnetism and synthesizability remain elusive. Here, heuristics or leveraging experimental data for ML offer promising alternatives.[8,9]
T2 - Advanced Materials Safety 2025
CY - Dresden, Germany
DA - 04.11.2025
KW - Nano Particles
KW - Machine Learning
KW - Automation
KW - Materials Design
KW - Sustainability
KW - Material Safety
PY - 2025
AN - OPUS4-64599
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Von der Computerchemie zur Materialinformatik
N2 - In dem Vortrag stelle ich meinen Lebenslauf von einem Chemiestudium bis hin zur Materialinformatik vor. Hierbei stelle ich unter anderem Computerchemie und Materialinformatik vor. Das beinhaltet Kurzeinführungen in die Quantenmechanik und das maschinelle Lernen.
T2 - Landesseminar Berlin Brandenburg der deutschen Auswahl für die Internationale Chemieolympiade (Schülerwettbewerb)
CY - Berlin, Germany
DA - 17.10.2025
KW - Materials Design
KW - Computational Chemistry
KW - Materials Acceleration Platforms
KW - Automation
KW - Machine Learning Potentials
KW - Outreach
PY - 2025
AN - OPUS4-64412
LA - deu
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Janssen, Jan
A1 - George, Janine
A1 - Geiger, Julian
A1 - Bercx, Marnik
A1 - Wang, Xing
A1 - Ertural, Christina
A1 - Schaarschmidt, Joerg
A1 - Ganose, Alex M.
A1 - Pizzi, Giovanni
A1 - Hickel, Tilmann
A1 - Neugebauer, Joerg
T1 - A Python workflow definition for computational materials design
N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements
the PWD in AiiDA, jobflow, and pyiron.
KW - Automation
KW - Workflows
KW - Materials Design
KW - Multi-scale simulation
KW - Digitalization
PY - 2025
DO - https://doi.org/10.5281/zenodo.15516179
PB - Zenodo
CY - Geneva
AN - OPUS4-63233
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - INPR
A1 - Naik, Aakash A.
A1 - Dhamrait, Nidal
A1 - Ueltzen, Katharina
A1 - Ertural, Christina
A1 - Benner, Philipp
A1 - Rignanese, Gian-Marco
A1 - George, Janine
T1 - A critical assessment of bonding descriptors for predicting materials properties
N2 - Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuable concept for predicting materials properties. Over the years, various theoretical frameworks have been developed to characterize bonding in solid-state materials. However, integrating bonding information from these frameworks into machine learning pipelines at scale has been limited by the lack of a systematically generated and validated database. Recent advances in high-throughput bonding analysis workflows have addressed this issue, and our previously computed Quantum-Chemical Bonding Database for Solid-State Materials was extended to include approximately 13,000 materials. This database is then used to derive a new set of quantum-chemical bonding descriptors. A systematic assessment is performed using statistical significance tests to evaluate how the inclusion of these descriptors influences the performance of machine-learning models that otherwise rely solely on structure- and composition-derived features. Models are built to predict elastic, vibrational, and thermodynamic properties typically associated with chemical bonding in materials. The results demonstrate that incorporating quantum-chemical bonding descriptors not only improves predictive performance but also helps identify intuitive expressions for
properties such as the projected force constant and lattice thermal conductivity via symbolic regression.
KW - Bonding Analysis
KW - Machine Learning
KW - Symbolic Regression
KW - Chemical Understanding
KW - Phonons
KW - Thermal Conductivity
PY - 2026
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-655150
DO - https://doi.org/10.48550/arXiv.2602.12109
SP - 1
EP - 28
PB - Cornell University
CY - Ithaca, NY
AN - OPUS4-65515
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design
N2 - My talk covered, among other things, robust data generation for machine learning. It showed how heuristics can be used within machine learning models and how they might also be extracted from machine learning models. Beyond this, I showed an automated pipeline for training machine learning potentials.
T2 - Workshop on AI in Sustainable Materials Science
CY - Düsseldorf, Germany
DA - 27.01.2026
KW - Automation
KW - Digitalisation
KW - Materials Design
KW - Thermal Conductivity
KW - Chemical bonding
KW - Materials Acceleration Platforms
PY - 2026
AN - OPUS4-65427
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design
N2 - My talk covered, among other things, robust data generation for machine learning. It showed how heuristics can be used within machine learning models and how they might also be extracted from machine learning models. Beyond this, I showed an automated pipeline for training machine learning potentials.
T2 - Seminar Gruppe Stephan Roche
CY - Barcelona, Spain
DA - 22.01.2026
KW - Automation
KW - Machine Learning
KW - Materials Acceleration Platforms
KW - Thermal Conductivity
KW - Phonons
KW - Bonding Analysis
PY - 2026
AN - OPUS4-65428
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - INPR
A1 - Janssen, Jan
A1 - George, Janine
A1 - Geiger, Julian
A1 - Bercx, Marnik
A1 - Wang, Xing
A1 - Ertural, Christina
A1 - Schaarschmidt, Joerg
A1 - Ganose, Alex M.
A1 - Pizzi, Giovanni
A1 - Hickel, Tilmann
A1 - Neugebauer, Joerg
T1 - A Python workflow definition for computational materials design
N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA, jobflow, and pyiron.
KW - Automation
KW - Workflow
KW - Materials Design
KW - Multi-scale simulation
KW - Digitalization
PY - 2025
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-632328
DO - https://doi.org/10.48550/arXiv.2505.20366
SP - 1
EP - 12
AN - OPUS4-63232
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Robust Data Generation, Heuristics and Machine Learning for Materials Design
N2 - This talk first introduces students to the Materials Acceleration Platforms and Advanced Materials Characterization at BAM. Then, it motivates high-throuhgput screening for materials discovery and advanced materials simulations based on these core topics. Then four different research studies are presentend: evaluation of generative models, synthesizability prediction via PU learning, acceleration of materials property predictions with bonding analysis and advanced materials simulations supported by automatically trained machine learning potentials.
T2 - Guest Lecture in MSE 403/1003, a Seminar in the Curriculum of the University of Toronto
CY - Online meeting
DA - 13.02.2026
KW - Automation
KW - Materials Acceleration Platforms
KW - Machine Learning
KW - Workflows
KW - Phonons
KW - Bonding Analysis
KW - Thermal Conductivity
PY - 2026
AN - OPUS4-65514
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Miliūtė, Aistė
A1 - Bustamante, Joana
A1 - Mieller, Björn
A1 - Stawski, Tomasz
A1 - George, Janine
A1 - Knoop, F.
T1 - High-quality zirconium vanadate samples for negative thermal expansion (NTE) analysis
N2 - Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-1. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock.
Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes.
Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating.
We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism.
T2 - TDEP2023: Finite-temperature and anharmonic response properties of solids in theory and practice
CY - Linköping, Sweden
DA - 21.08.2023
KW - NTE
KW - Sol-gel
KW - Solid-state
KW - Ab-initio
KW - TDEP
PY - 2023
AN - OPUS4-58135
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Miliūtė, Aistė
A1 - Bustamante, Joana
A1 - Mieller, Björn
A1 - Stawski, Tomasz
A1 - George, Janine
A1 - Knoop, F.
T1 - High-quality zirconium vanadate samples for negative thermal expansion (NTE) analysis
N2 - Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock.
Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes.
Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating.
We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism.
T2 - 4th International Symposium on Negative Thermal Expansion and Related Materials (ISNTE-4)
CY - Padua, Italy
DA - 04.07.2023
KW - NTE
KW - Sol-gel
KW - Solid-state
KW - Ab-initio
KW - TDEP
PY - 2023
AN - OPUS4-58132
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Miliūtė, Aistė
A1 - Bustamante, Joana
A1 - Mieller, Björn
A1 - Stawski, Tomasz
A1 - George, Janine
A1 - Knoop, F.
T1 - High-quality zirconium vanadate samples for negative thermal expansion (NTE) analysis
N2 - Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock.
Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes.
Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating.
We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism.
T2 - 16th International conference on materials chemistry (MC16)
CY - Dublin, Ireland
DA - 03.07.2023
KW - NTE
KW - Sol-gel
KW - Solid-state
KW - Ab-initio
KW - TDEP
PY - 2023
AN - OPUS4-58134
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Miliūtė, Aistė
A1 - George, Janine
A1 - Mieller, Björn
A1 - Stawski, Tomasz
T1 - ZrV2O7 negative thermal expansion (NTE) material
N2 - Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material that exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). Therefore, it can be used to create composites with controllable expansion coefficients and prevent thermal stress, fatigue, cracking, and deformation at interfaces. We implement interdisciplinary research to analyze such material. We study the influence of the synthesis methods and their parameters on the sample's purity, crystallinity, and homogeneity. Moreover, we implement ab initio-based vibrational computations with partially treated anharmonicity in combination with experimental methods to follow temperature-induced structural changes and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder.
T2 - SALSA Make and Measure Conference
CY - Berlin, Germany
DA - 13.09.2023
KW - NTE
KW - Composites
KW - TEM
PY - 2023
AN - OPUS4-58367
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Rosen, Andrew S.
A1 - Gallant, Max
A1 - George, Janine
A1 - Riebesell, Janosh
A1 - Sahasrabuddhe, Hrushikesh
A1 - Shen, Jimmy-Xuan
A1 - Wen, Mingjian
A1 - Evans, Matthew L.
A1 - Petretto, Guido
A1 - Waroquiers, David
A1 - Rignanese, Gian-Marco
A1 - Persson, Kristin A.
A1 - Jain, Anubhav
A1 - Ganose, Alex M.
T1 - Jobflow: Computational Workflows Made Simple
N2 - Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager.
Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases.
KW - Automation
KW - Workflows
KW - Computational Materials Science
PY - 2024
DO - https://doi.org/10.5281/zenodo.10466868
PB - Zenodo
CY - Geneva
AN - OPUS4-59313
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Ueltzen, Katharina
A1 - Naik, Aakash
A1 - Ertural, Christina
A1 - Benner, Philipp
A1 - George, Janine
T1 - Software and data repository: Can simple exchange heuristics guide us in predicting magnetic properties of solids?
N2 - Software and data for the publication "Can simple exchange heuristics guide us in predicting magnetic properties of solids?" Release that corresponds to the first preprint version of the article. Full Changelog: https://github.com/DigiMatChem/paper-exchange-heuristics-in-magnetic-materials/commits/v1.0.0
KW - Magnetism
KW - Machine Learning
KW - Materials Design
KW - Chemically Complex Materials
KW - Sustainable Materials Design
PY - 2025
DO - https://doi.org/10.5281/zenodo.16811104
PB - Zenodo
CY - Geneva
AN - OPUS4-64672
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Ganose, Alex M.
A1 - Sahasrabuddhe, Hrushikesh
A1 - Asta, Mark
A1 - Beck, Kevin
A1 - Biswas, Tathagata
A1 - Bonkowski, Alexander
A1 - Bustamante, Joana
A1 - Chen, Xin
A1 - Chiang, Yuan
A1 - Chrzan, Daryl C.
A1 - Clary, Jacob
A1 - Cohen, Orion A.
A1 - Ertural, Christina
A1 - Gallant, Max C.
A1 - George, Janine
A1 - Gerits, Sophie
A1 - Goodall, Rhys E. A.
A1 - Guha, Rishabh D.
A1 - Hautier, Geoffroy
A1 - Horton, Matthew
A1 - Inizan, T. J.
A1 - Kaplan, Aaron D.
A1 - Kingsbury, Ryan S.
A1 - Kuner, Matthew C.
A1 - Li, Bryant
A1 - Linn, Xavier
A1 - McDermott, Matthew J.
A1 - Mohanakrishnan, Rohith Srinivaas
A1 - Naik, Aakash A.
A1 - Neaton, Jeffrey B.
A1 - Parmar, Shehan M.
A1 - Persson, Kristin A.
A1 - Petretto, Guido
A1 - Purcell, Thomas A. R.
A1 - Ricci, Francesco
A1 - Rich, Benjamin
A1 - Riebesell, Janosh
A1 - Rignanese, Gian-Marco
A1 - Rosen, Andrew S.
A1 - Scheffler, Matthias
A1 - Schmidt, Jonathan
A1 - Shen, Jimmy-Xuan
A1 - Sobolev, Andrei
A1 - Sundararaman, Ravishankar
A1 - Tezak, Cooper
A1 - Trinquet, Victor
A1 - Varley, Joel B.
A1 - Vigil-Fowler, Derek
A1 - Wang, Duo
A1 - Waroquiers, David
A1 - Wen, Mingjian
A1 - Yang, Han
A1 - Zheng, Hui
A1 - Zheng, Jiongzhi
A1 - Zhu, Zhuoying
A1 - Jain, Anubhav
T1 - Correction: Atomate2: Modular workflows for materials science
N2 - Correction for “Atomate2: modular workflows for materials science” by Alex M. Ganose et al., Digital Discovery, 2025, 4, 1944–1973, https://doi.org/10.1039/D5DD00019J.
PY - 2025
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-640297
DO - https://doi.org/10.1039/d5dd90036k
SN - 2635-098X
SP - 1
EP - 2
PB - Royal Society of Chemistry (RSC)
CY - Cambridge
AN - OPUS4-64029
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Crossing Scientific Disciplines with Materials Informatics:� From Atoms to Algorithms
N2 - Within this talk, I introduced students from Physics to Materials Informatics. To provide a context for this research, I have introduced the students to BAM and its tasks. I then started to introduce our activity field materials design, including materials acceleration platforms. Then, I explained how simulations speed up the materials searches as parf of materials acceleration platforms.
T2 - jDPG Jena Meeting - Poland exchange
CY - Jena, Germany
DA - 26.02.2026
KW - Automation
KW - Machine Learning
KW - Materials Design
KW - Batteries
KW - Workflows
PY - 2026
AN - OPUS4-65589
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Openness, Curiosity and Identity
N2 - I am introducing early-career researchers to my view on the academic career. I highlighted openness with regard to people. I mentioned my interest in multiple fields of the natural sciences, driving my curiosity. I additionally highlight my core belief that science is a team sport.
T2 - Open Minds – Exploring Science Careers
CY - Berlin, Germany
DA - 05.03.2026
KW - Materials Informatics
PY - 2026
AN - OPUS4-65632
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Robust data generation, heuristics and machine learning for materials design
N2 - Machine learning (ML) offers new routes to overcome the limitations of density functional theory (DFT) for advanced materials. We present data-generation strategies and workflows for ML interatomic potentials, including large-scale quantum-chemical bonding analysis.[1,2,3] Incorporating bonding descriptors into ML models enables prediction of phononic properties and validation of correlations between bonding strength, force constants, and thermal conductivity.[3] We introduce autoplex, an automated framework for training ML potentials, supporting general-purpose and phonon-focused workflows.[4] These developments provide a basis for fine-tuning foundation models for thermal transport at reduced cost.[5] For properties such as magnetism or synthesizability, we discuss complementary approaches, comparing ab initio methods with chemical heuristics and experimental data-driven ML models.[6,7]Our work advances scalable, accurate simulations for materials discovery.
T2 - DPG Dresden, Condensed Matter
CY - Dresden, Germany
DA - 10.03.2026
KW - Materials Design
KW - Materials Acceleration Platforms
KW - Advanced materials simulations
KW - Automation
KW - High-throughput
KW - Thermal Conductivity
KW - Chemical Bonding Analysis
KW - Phonons
KW - Amorphous Materials
PY - 2026
AN - OPUS4-65648
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -