TY - GEN
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 (JSONS: Part 2)
N2 - This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary.
KW - Bonding analysis
KW - Automation
KW - Materials Informatics
KW - Computational Chemistry
KW - Database
PY - 2023
DO - https://doi.org/10.5281/zenodo.7821727
PB - Zenodo
CY - Geneva
AN - OPUS4-57440
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 - A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 1)
N2 - This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary.
KW - Bonding analysis
KW - Automation
KW - Materials Informatics
KW - Computational Chemistry
KW - Database
PY - 2023
DO - https://doi.org/10.5281/zenodo.7794811
PB - Zenodo
CY - Geneva
AN - OPUS4-57439
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 1)
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)
Refer to README.md file instructions to reproduce the data.
KW - Bonding analysis
KW - Automation
KW - Materials Informatics
KW - Computational Chemistry
KW - Database
PY - 2023
DO - https://doi.org/10.5281/zenodo.7852082
PB - Zenodo
CY - Geneva
AN - OPUS4-57441
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 3)
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.7852791
PB - Zenodo
CY - Geneva
AN - OPUS4-57443
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 4)
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.7852798
PB - Zenodo
CY - Geneva
AN - OPUS4-57444
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 2)
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.7852107
PB - Zenodo
CY - Geneva
AN - OPUS4-57442
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Naik, Aakash
T1 - Building quantum chemical orbital based bonding descriptor database
N2 - Motivation, methodology and and results of our quantum chemical bonding descriptors database presented in form of a Poster
T2 - RSC Twitter Conference 2023
CY - Online meeting
DA - 28.02.2023
KW - Bonding analysis
KW - Automation
KW - Materials Informatics
KW - Computational Chemistry
KW - Database
PY - 2023
UR - https://twitter.com/NaikAak/status/1630400167080869893
UR - https://twitter.com/NaikAak/status/1630540436434558977
AN - OPUS4-57101
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
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 - 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 - Naik, Aakash
T1 - Material property predictions by incorporating quantum chemical bonding information
N2 - Interactions between constituent atoms in crystalline materials have been shown to influence the properties of materials, such as elasticity, ionic and thermal conductivity, etc.[1–3] These interactions between constituent atoms, often quantified as bond strengths, can be extracted from crystalline materials using density-based[4], energy-based[5], and orbital-based methods. LOBSTER[6] is a software that relies on the orbital-based method to extract such bonding information by projecting the plane wave-based wave functions of modern density functional theory computations (DFT) onto a local atomic orbital basis. To garner a better understanding of how this bonding information relates to material properties on a larger scale, machine learning seems an obvious choice. However, for such data-driven studies, large quantities of data that are systematically generated, validated, and post-processed (feature engineering) in a form suitable for input in state-of-the-art ML models are often needed.[7] Here, we first present a workflow implemented in atomate2[8] that can generate such bonding-related data using the LOBSTER program with minimal user input and a post-processing tool, LobsterPy[9], which can summarize and engineer features that could be directly used as input for ML studies. Lastly, we demonstrate the utility of these newly generated features by building a simple machine-learned model to predict harmonic phonon properties using the bonding dataset[10] generated by us for 1500 materials. We find a clear correlation between the bonding information and the phonon property.
T2 - STC 2024
CY - Braunschweig, Germany
DA - 02.09.2024
KW - Bonding analysis
KW - Machine learning
KW - Feature engineering
PY - 2024
AN - OPUS4-61130
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Naik, Aakash
T1 - Enhancing material property predictions using quantum chemical bonding descriptors
N2 - The properties of crystalline materials, such as elasticity, ionic conductivity, and thermal conductivity, are influenced by interactions between their constituent atoms.[1–3] These interactions, which are often quantified in terms of bond strength, can be extracted from crystalline materials using density-based[4], energy-based[5] and orbital-based methods. LOBSTER[6] is a software that relies on the orbital-based method to extract such bonding information by projecting the plane wave-based wave functions of modern density functional theory computations (DFT) onto a local atomic orbital basis. To garner a better understanding of how this bonding information relates to material properties on a larger scale, machine learning seems an obvious choice. However, for such data-driven studies, large quantities of data need to be systematically generated, validated, and post-processed (e.g., by feature engineering), as they can only then be used as input for state-of-the-art ML models. We have, therefore, previously developed workflows for high-throughput bonding analysis[7]. In this work, we use the results[8] from high-throughput LOBSTER calculations using our workflows to generate bonding-based features. To extract such features from the LOBSTER computations, we use our package LobsterPy.[9] The importance of these features is then tested by employing them in several state-of-the-art ML algorithms and architectures to predict the mechanical and vibrational properties of crystalline materials. We show that including these bonding-based features alongside typical composition and structure-based features helps enhance the model’s predictive accuracy.
T2 - 18th German Conference on Cheminformatics
CY - Bad Soden am Taunus, Germany
DA - 03.11.2024
KW - Bonding analysis
KW - Machine learning
KW - Materials Descriptors
PY - 2024
AN - OPUS4-62217
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Naik, Aakash
T1 - Harnessing quantum chemical bonding analysis descriptors for material property predictions
N2 - Examining the bonding between their constituent atoms in crystalline materials has played a vital role in understanding material properties.[1–4] For instance, low thermal conductivity in materials is typically attributed to its anharmonicity, which has been reported to arise from strong antibonding interactions and local environment distortions.[5–7] The bonds in the material are often quantified in terms of bond strength and can be extracted from crystalline materials using density-based[8], energy-based[9], and orbital-based[10] methods. LOBSTER[11] is a program that relies on an orbital-based method to extract such bonding information by projecting the plane wave-based wave functions of modern density functional theory computations (DFT) onto a local atomic orbital basis. Since our goal was to use bonding analysis descriptors for material property predictions, we needed to first systematically generate large quantities of bonding analysis data. To streamline this process, we have developed a user-friendly workflow[12], which is now also part of the atomate2[13] package that can generate bonding information data extracted using the LOBSTER program for crystalline materials. This workflow requires only the structure as input from the user. Employing this workflow, we have generated for ~13000 crystalline compounds such bonding analysis data. To create new descriptors from these data, we use our package LobsterPy.[14] The curated descriptors span different types, including statistical representations of bonding characteristics for traditional ML algorithms (e.g., random forests), textual descriptions for large language models (LLMs), and structure graphs for graph neural networks (GNNs). These descriptors are then tested by employing them in several state-of-the-art ML algorithms and architectures to predict the mechanical, vibrational, and thermal properties of crystalline materials. Through this work, we are not only able to demonstrate how one can enhance the model’s predictive accuracy[15] by incorporating quantum chemical bonding-based descriptors alongside typical composition and structure-based descriptors but it also aids in uncovering relationships between bonding and materials properties on a larger scale, which was not possible before.
T2 - MRS SPRING 2025
CY - Seattle, WA, USA
DA - 07.04.2025
KW - Bonding analysis
KW - Machine learning
KW - Materials Descriptors
PY - 2025
AN - OPUS4-63001
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 - Naik, Aakash Ashok
T1 - Linking quantum chemical bonding analysis descriptors to material property predictions
N2 - Examining the bonding between their constituent atoms in crystalline materials has played a vital role in understanding material properties. For instance, low thermal conductivity in materials is typically attributed to its anharmonicity, which has been reported to arise from strong antibonding interactions and local environment distortions. Employing an automated for bonding analysis that we developed, we have generated for ~13000 crystalline compounds such bonding analysis data. To create new descriptors from these data automatically, we extended our package LobsterPy. The curated descriptors span different types, including statistical representations of bonding characteristics for traditional ML algorithms (e.g., random forests), textual descriptions for large language models (LLMs), and structure graphs for graph neural networks (GNNs). These descriptors are then tested by employing them in several state-of-the-art ML algorithms and architectures to predict the mechanical, vibrational, and thermal properties of crystalline materials. Through this work, we are not only able to demonstrate how one can enhance the model’s predictive accuracy by incorporating quantum chemical bonding-based descriptors alongside typical composition and structure-based descriptors, but it also aids in uncovering relationships between bonding and materials properties on a larger scale, which was not possible before.
T2 - AI MSE 2025
CY - Bochum, Germany
DA - 18.11.2025
KW - Bonding analysis
KW - Machine learning
KW - Materials Descriptors
KW - Force constants
PY - 2025
AN - OPUS4-64791
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
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
A1 - Gangan, Abhijeet S
A1 - George, Janine
A1 - Harb, Hassan
A1 - Khalighinejad, Ghazal
A1 - Takrim Khan, Sartaaj
A1 - Klawohn, Sascha
A1 - Lederbauer, Magdalena
A1 - Mahjoubi, Soroush
A1 - Mohr, Bernadette
A1 - Mohamad Moosavi, Seyed
A1 - Naik, Aakash Ashok
A1 - Ozhan, Aleyna Beste
A1 - Plessers, Dieter
A1 - Roy, Aritra
A1 - Schoeppach, 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 - 32 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 - Large Language Models
KW - Machine Learning
KW - Materials Design
KW - Bonding Analysis
KW - Phonons
KW - Thermal properties
PY - 2025
DO - https://doi.org/10.1088/2632-2153/ae011a
SN - 2632-2153
VL - 6
IS - 3
SP - 1
EP - 34
PB - IOP Publishing
AN - OPUS4-64019
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
A1 - Batatia, Ilyes
A1 - Benner, Philipp
A1 - Chiang, Yuan
A1 - Elena, Alin M.
A1 - Kovács, Dávid P.
A1 - Riebesell, Janosh
A1 - Advincula, Xavier R.
A1 - Asta, Mark
A1 - Avaylon, Matthew
A1 - Baldwin, William J.
A1 - Berger, Fabian
A1 - Bernstein, Noam
A1 - Bhowmik, Arghya
A1 - Bigi, Filippo
A1 - Blau, Samuel M.
A1 - Cărare, Vlad
A1 - Ceriotti, Michele
A1 - Chong, Sanggyu
A1 - Darby, James P.
A1 - De, Sandip
A1 - Della Pia, Flaviano
A1 - Deringer, Volker L.
A1 - Elijošius, Rokas
A1 - El-Machachi, Zakariya
A1 - Fako, Edvin
A1 - Falcioni, Fabio
A1 - Ferrari, Andrea C.
A1 - Gardner, John L. A.
A1 - Gawkowski, Mikołaj J.
A1 - Genreith-Schriever, Annalena
A1 - George, Janine
A1 - Goodall, Rhys E. A.
A1 - Grandel, Jonas
A1 - Grey, Clare P.
A1 - Grigorev, Petr
A1 - Han, Shuang
A1 - Handley, Will
A1 - Heenen, Hendrik H.
A1 - Hermansson, Kersti
A1 - Ho, Cheuk Hin
A1 - Hofmann, Stephan
A1 - Holm, Christian
A1 - Jaafar, Jad
A1 - Jakob, Konstantin S.
A1 - Jung, Hyunwook
A1 - Kapil, Venkat
A1 - Kaplan, Aaron D.
A1 - Karimitari, Nima
A1 - Naik, Aakash A.
A1 - Csányi, Gábor
T1 - A foundation model for atomistic materials chemistry
N2 - Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.
KW - Materials Design
KW - Thermal Conducitivity
KW - Nanoparticles
KW - Batteries
PY - 2025
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-647829
DO - https://doi.org/10.1063/5.0297006
SN - 0021-9606
VL - 163
IS - 18
SP - 1
EP - 89
PB - AIP Publishing
AN - OPUS4-64782
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
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 - 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 - Materials design
KW - DFT workflows
KW - Phonons
KW - Thermal conductivity
KW - Bonding analysis
PY - 2025
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-635759
DO - https://doi.org/10.1039/d5dd00019j
SN - 2635-098X
SP - 1
EP - 30
PB - Royal Society of Chemistry (RSC)
CY - Cambridge
AN - OPUS4-63575
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 -