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 - 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 - 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 -
TY - INPR
A1 - Zimmermann, Yoel
A1 - Bazgir, Adib
A1 - Al-Feghali, Alexander
A1 - Ansari, Mehrad
A1 - Bocarsly, Joshua
A1 - Brinson, L. Catherine
A1 - Chiang, Yuan
A1 - Circi, Defne
A1 - Chiu, Min-Hsueh
A1 - Daelman, Nathan
A1 - Evans, Matthew L.
A1 - Gangan, Abhijeet S.
A1 - George, Janine
A1 - Harb, Hassan
A1 - Khalighinejad, Ghazal
A1 - Khan, Sartaaj Takrim
A1 - Klawohn, Sascha
A1 - Lederbauer, Magdalena
A1 - Mahjoubi, Soroush
A1 - Mohr, Bernadette
A1 - Moosavi, Seyed Mohamad
A1 - Naik, Aakash
A1 - Ozhan, Aleyna Beste
A1 - Plessers, Dieter
A1 - Roy, Aritra
A1 - Schöppach, Fabian
A1 - Schwaller, Philippe
A1 - Terboven, Carla
A1 - Ueltzen, Katharina
A1 - Wu, Yue
A1 - Zhu, Shang
A1 - Janssen, Jan
A1 - Li, Calvin
A1 - Foster, Ian
A1 - Blaiszik, Ben
T1 - 34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
N2 - Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 34 total projects developed during the second annual Large Language Model Hackathon for Applications in Materials Science and Chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.
KW - Automation
KW - LLM
KW - Machine Learning
KW - Agent
KW - Bonding Analysis
KW - Materials Searches
KW - Finetuning
PY - 2025
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-631720
DO - https://doi.org/10.48550/arXiv.2505.03049
SP - 1
EP - 33
AN - OPUS4-63172
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - INPR
A1 - Ganose, Alex
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
A1 - Clary, Jacob
A1 - Cohen, Orion
A1 - Ertural, Christina
A1 - George, Janine
A1 - Gallant, Max
A1 - George, Janine
A1 - Gerits, Sophie
A1 - Goodall, Rhys
A1 - Guha, Rishabh
A1 - Hautier, Geoffroy
A1 - Horton, Matthew
A1 - Kaplan, Aaron
A1 - Kingsbury, Ryan
A1 - Kuner, Matthew
A1 - Li, Bryant
A1 - Linn, Xavier
A1 - McDermott, Matthew
A1 - Rohith Srinivaas Mohanakrishnan,
A1 - Naik, Aakash
A1 - Neaton, Jeffrey
A1 - Persson, Kristin
A1 - Petretto, Guido
A1 - Purcell, Thomas
A1 - Ricci, Francesco
A1 - Rich, Benjamin
A1 - Riebesell, Janosh
A1 - Rignanese, Gian-Marco
A1 - Rosen, Andrew
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
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 - Atomate2: Modular workflows for materials science
N2 - High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2’s improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
KW - Automation
KW - DFT
KW - Digitalisation
KW - Materials design
KW - Machine learning
KW - Machine learned interatomic potentials
PY - 2025
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624487
DO - https://doi.org/10.26434/chemrxiv-2025-tcr5h
SN - 2573-2293
SP - 1
EP - 66
PB - American Chemical Society (ACS)
CY - Washington, D.C.
AN - OPUS4-62448
LA - eng
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 - 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 - Group Seminar at the University of Toronto
CY - Online Meeting
DA - 18.07.2025
KW - Automation
KW - Magnetism
KW - High-throughput
KW - Amorphous Materials
KW - Phase Change Materials
KW - Phonons
PY - 2025
AN - OPUS4-63743
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - VIDEO
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 - Materials Design
KW - Machine Learning
KW - Chemical Bonding
KW - Batteries
KW - Amorphous Materials
KW - Workflows
KW - Machine Learned Interatomic Potentials
KW - Phonons
KW - Magnetism
KW - Synthesizability
PY - 2025
UR - https://www.youtube.com/watch?v=Sfco48s1fpU
PB - YouTube, LLC
CY - San Bruno, CA, USA
AN - OPUS4-63744
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Grandel, Jonas
A1 - George, Janine
T1 - Mace-mp-03b Phonon Benchmark
N2 - This repository contains phonon calculations and evaluations for the MACE MP-0b3 model. First release of the scripts used for the phonon benchmark in the paper benchmarking the MACE-MP-03b model. See https://arxiv.org/abs/2401.00096 for a previous version of the paper. Full Changelog: https://github.com/JaGeo/mace-mp-03b-phonon-benchmark/commits/v0.0.1
KW - Machine Learned Interatomic Potentials
KW - Phonons
KW - Thermal Conductivity
KW - Materials Searches
KW - Foundation Model
PY - 2025
DO - https://doi.org/10.5281/zenodo.15462975
PB - Zenodo
CY - Geneva
AN - OPUS4-63174
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - High-throughput Computational Screening
N2 - I provided a tutorial lecture on high-throughput DFT calculations in combination with heuristics, machine learning and workflows. I started with the introduction of the software infrastructure. Then, I showed applications for phonon predictions, magnetism and synthesizability prediction.
T2 - Machine Learning for Materials 2025
CY - Karlsruhe, Germany
DA - 08.09.2025
KW - Materials Design
KW - Automation
KW - High-throughput
KW - Magnetism
KW - Thermal conductivity
PY - 2025
AN - OPUS4-64074
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - Introduction to computational methods with periodic boundaries
N2 - I provided an introduction to density functional theory (DFT) with periodic boundary conditions. I repeated the derivation of DFT and the Bloch theorem. Then, I combined both concepts. Additionally, I delivered an outlook to automatic DFT for solids and machine learning in the field.
T2 - Computational Methods in Crystallography
CY - Lausanne, Switzerland
DA - 09.09.2025
KW - DFT
KW - Automation
KW - Machine Learning
KW - Bloch Theorem
KW - Materials Design
PY - 2025
AN - OPUS4-64044
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - George, Janine
T1 - High-throughput Computational Screening With Chemical Heuristics, Workflows, and Machine Learning
N2 - Within my talk, I have introduced the audience to high-throughput materials discovery with the help of workflow tools, machine learning and chemical heuristics. I started with a general introduction to the Materials Project software infrastructure and databases such as the Materials Project and NOMAD. Then, I dived into machine learning tasks relying on chemical bonding information, machine-learned interatomic potentials, and experimental data. Target properties of the machine-learning tasks were thermal properties, phonons, magnetism and synthesizability.
T2 - Seminar at the Inorganic Chemistry Laboratory in Oxford
CY - Oxford, United Kingdom
DA - 25.09.2025
KW - High-throughput
KW - Sustainability
KW - Machine Learning
KW - Materials Design
KW - Thermal Conductivity
KW - Magnetism
KW - Synthesizability
KW - Materials Acceleration Platform
PY - 2025
AN - OPUS4-64217
LA - eng
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 - 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 - 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 - INPR
A1 - George, Janine
A1 - Naik, Aakash
A1 - Ueltzen, Katharina
T1 - Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
N2 - Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.
KW - Large Language Models
KW - Materials Design
KW - Materials Properties
KW - Phonons
KW - Bonding Analysis
KW - Machine Learning
PY - 2024
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622113
DO - https://doi.org/10.48550/arXiv.2411.15221
SP - 1
EP - 98
AN - OPUS4-62211
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - INPR
A1 - Liu, Yuanbin
A1 - Morrow, Joe D.
A1 - Ertural, Christina
A1 - Fragapane, Natascia L.
A1 - Gardner, John L. A.
A1 - Naik, Aakash
A1 - Zhou, Yuxing
A1 - George, Janine
A1 - Deringer, Volker L.
T1 - An automated framework for exploring and learning potential-energy surfaces
N2 - Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex ('automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO2, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning -- with a long-term vision of making it a genuine mainstream tool in physics, chemistry, and materials science.
KW - Machine learned interatomic potentials
KW - Ab initio
KW - DFT
KW - Automation
KW - Workflows
KW - Machine learning
KW - Materials design
KW - Materials discovery
PY - 2024
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623188
DO - https://doi.org/10.48550/arXiv.2412.16736
SN - 2331-8422
SP - 1
EP - 27
PB - Cornell University
CY - Ithaca, NY
AN - OPUS4-62318
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
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 - We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch
other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow
workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.
KW - Automation
KW - Workflow
KW - Computational Materials Science
KW - Computations
KW - Software
PY - 2024
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593104
DO - https://doi.org/10.21105/joss.05995
VL - 9
IS - 93
SP - 1
EP - 7
PB - The Open Journal
AN - OPUS4-59310
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - INPR
A1 - Amariamir, Sasan
A1 - George, Janine
A1 - Benner, Philipp
T1 - SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
N2 - Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materials, remains a complex challenge due to the limitations of traditional heuristics and thermodynamic proxies. While stability metrics such as formation energy offer partial insights, they fail to account for kinetic factors and technological constraints that influence synthesis outcomes. These challenges are further compounded by the scarcity of negative data, as failed synthesis attempts are often unpublished or context-specific.
We present SynCoTrain, a semi-supervised machine learning model designed to predict the synthesizability of materials. SynCoTrain employs a co-training framework leveraging two complementary graph convolutional neural networks: SchNet and ALIGNN. By iteratively exchanging predictions between classifiers, SynCoTrain mitigates model bias and enhances generalizability. Our approach uses Positive and Unlabeled (PU) Learning to address the absence of explicit negative data, iteratively refining predictions through collaborative learning.
The model demonstrates robust performance, achieving high recall on internal and leave-out test sets. By focusing on oxide crystals, a well-characterized material family with extensive experimental data, we establish SynCoTrain as a reliable tool for predicting synthesizability while balancing dataset variability and computational efficiency. This work highlights the potential of co-training to advance high-throughput materials discovery and generative research, offering a scalable solution to the challenge of synthesizability prediction.
KW - Materials Design
KW - Materials Discovery
KW - Synthesizability
KW - Machine Learning
KW - Cotraining
KW - New Materials
KW - Materials Acceleration Platforms
PY - 2024
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622104
DO - https://doi.org/10.48550/arXiv.2411.12011
SP - 1
EP - 39
AN - OPUS4-62210
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Ghosh, Sheuly
A1 - Ueltzen, Katharina
A1 - George, Janine
A1 - Neugebauer, Jörg
A1 - Körmann, Fritz
T1 - Chemical ordering and magnetism in face-centered cubic CrCoNi alloy
N2 - The impact of magnetism on chemical ordering in face-centered cubic CrCoNi medium entropy alloy is studied by a combination of ab initio simulations, machine learning potentials, and Monte Carlo simulations. Large magnetic energies are revealed for some mixed L12 /L10 type ordered configurations, which are rooted in strong nearest-neighbor magnetic exchange interactions and chemical bonding among the constituent elements. There is a delicate interplay between magnetism and stability of MoPt2 and L12 /L10 type of ordering which may explain opposing experimental and theoretical findings.
KW - Bonding analysis
KW - Alloys
KW - Magnetism
KW - Material design
PY - 2024
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-600040
DO - https://doi.org/10.21203/rs.3.rs-3978660/v1
SN - 2693-5015
SP - 1
EP - 13
PB - Research Square Platform LLC
CY - Durham, NC
AN - OPUS4-60004
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
A1 - Naik, Aakash
A1 - Ueltzen, Katharina
A1 - Ertural, Christina
A1 - Jackson, Adam J.
A1 - George, Janine
T1 - LobsterPy: A package to automatically analyze LOBSTERruns
N2 - The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API.
KW - Materials Science
KW - Automation
KW - Bonding Analysis
KW - Materials Properties
PY - 2024
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-595809
DO - https://doi.org/10.21105/joss.06286
VL - 9
IS - 94
SP - 1
EP - 4
PB - The Open Journal
AN - OPUS4-59580
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 -