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 8)
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.7852823
PB - Zenodo
CY - Geneva
AN - OPUS4-57448
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Bustamante, Joana
A1 - Naik, Aakash
A1 - Ertural, Christina
A1 - Ueltzen, Katharina
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, a new low-temperature (LT) phase transition of canfieldite at 120K has been found. Here, 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. Thermal properties such as the constant-pressure heat capacity (Cp) and thermal conductivity are very close to experimental measurements. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with argyrodites analogues, Ag8XS6 (X = Sn, Si, Ge), to arrive at an improved
T2 - 16th International conference on materials chemistry (MC16)
CY - Dublin, Ireland
DA - 03.07.2023
KW - Thermoelectric materials
KW - DFT
KW - QHA
KW - Grüneisen parameter
PY - 2023
AN - OPUS4-57887
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
A1 - Liu, Yuanbin
A1 - Morrow, Joe D.
A1 - Ertural, Christina
A1 - Fragapane, Natascia L.
A1 - Gardner, John L. A.
A1 - Naik, Aakash A.
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 (‘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 in computational materials science.
KW - Automation
KW - Machine Learning
KW - Machine learning potentials
KW - Amorphous materials
KW - High-throughput
KW - Ab initio
KW - Materials property prediction
PY - 2025
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-639882
DO - https://doi.org/10.1038/s41467-025-62510-6
SN - 2041-1723
VL - 16
IS - 1
SP - 1
EP - 12
PB - Springer Science and Business Media LLC
AN - OPUS4-63988
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
A1 - George, Janine
A1 - Petretto, G.
A1 - Naik, Aakash
A1 - Esters, M.
A1 - Jackson, A. J.
A1 - Nelson, R.
A1 - Dronskowski, R.
A1 - Rignanese, G.-M.
A1 - Hautier, G.
T1 - Automated bonding analysis with crystal orbital Hamilton populuations
N2 - Automated bonding analysis software has been developed based on Crystal Orbital Hamilton Populations to facilitate high-throughput bonding analysis and machine-learning of bonding features. This work presents the software and discusses its applications to simple and complex materials such as GaN, NaCl, the oxynitrides XTaO2N (X=Ca, Ba, Sr) and Yb14Mn1Sb11.
KW - Chemical bonds
KW - Automation
KW - High-throughput
PY - 2022
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-551641
DO - https://doi.org/10.1002/cplu.202200123
SN - 2192-6506
SP - 1
EP - 11
PB - Wiley-VCH
CY - Weinheim
AN - OPUS4-55164
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
A1 - Arnold, M.
A1 - Katzmann, J.
A1 - Naik, Aakash
A1 - Görne, A. L.
A1 - Härtling, Thomas
A1 - George, Janine
A1 - Schuster, C.
T1 - Investigations on electron beam irradiated rare-earth doped SrF2 for application as low fading dosimeter material: Evidence for and DFT simulation of a radiation-induced phase
N2 - A recent approach to measure electron radiation doses in the kGy range is the use of phosphors with an irradiation dose-dependent luminescence decay time. However, the applicability of the previously investigated material NaYF4:Yb3+,Er3+ is limited as it shows pronounced fading. Therefore, in this work, a modified SrF2 synthesis is presented that results in SrF2 nanoparticles codoped with Yb and either Er, Hm, or Tm. To assess their suitability as dosimeter material, dose response, as well as its degree of fading over 50 up to 140 days after irradiation were measured. Fading rates as small as 5% in SrF2:Er,Yb and 4% in SrF2:Ho,Yb were derived, which are comparable to established dosimeter materials. A combination of spectroscopy, diffraction and DFT calculations was used to elucidate the effect of irradiation, pointing towards the formation of a secondary phase of Yb2+ that we predict could be Yb2OF2. This irreversible formation of a secondary phase is considered to be the explanation for the low fading behavior in SrF2-based phosphors compared to NaYF4:Yb, Er, a highly attractive feature for electron beam dosimetry.
KW - DFT
KW - Structure prediction
PY - 2022
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-554115
DO - https://doi.org/10.1039/D2TC01773C
SN - 2050-7526
VL - 10
IS - 32
SP - 11579
EP - 11587
PB - RSC
CY - London
AN - OPUS4-55411
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - INPR
A1 - Pauw, Brian Richard
A1 - Laskina, Sofya
A1 - Naik, Aakash
A1 - Smales, Glen Jacob
A1 - George, Janine
A1 - Breßler, Ingo
A1 - Benner, Philipp
T1 - "Ultima Ratio": Simulating wide-range X-ray scattering and diffraction
N2 - We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is on the same scale as the scattering intensity, may offer a novel pathway for determining the degree of crystallinity.
The "Ultima Ratio" strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from Q < 0.01 1/nm up to Q < 150 1/nm, with a resolution of 0.16 Angstrom. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to 8000^3 voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-Q behaviour. The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder. While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts.
KW - X-ray
KW - Simulation
KW - 3D Fourier Transform
KW - High resolution
KW - XRD
KW - SAXS
KW - PDF
KW - Total scattering
KW - X-ray scattering
KW - Metal organic framework
KW - Electron density map
KW - FFT
PY - 2023
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-572067
DO - https://doi.org/10.48550/arXiv.2303.13435
VL - Cornell University
SP - 1
EP - 12
PB - Ithaca, NY
AN - OPUS4-57206
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 - 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 - Ueltzen, Katharina
A1 - Naik, Aakash A.
A1 - Ertural, Christina
A1 - Benner, Philipp
A1 - George, Janine
T1 - Can simple exchange heuristics guide us in predicting magnetic properties of solids?
N2 - A popular heuristic derived from the Kanamori-Goodenough-Anderson rules of superexchange connects bond angles and magnetism in certain transition metal compounds. We evaluate the fulfillment of this heuristic on databases of magnetic structures. The heuristic is partly satisfied in magnetic structures from experiment, and exceptions can be rationalized. We further show that incorporating this heuristic into machine learning models for magnetism improves the prediction.
KW - Magnetism
KW - Transition Metal Compounds
KW - Materials Design
KW - Sustainable Materials Design
KW - Machine Learning
KW - Materials Understanding
PY - 2025
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-639290
DO - https://doi.org/10.26434/chemrxiv-2025-xj84d
VL - Version 1
SP - 1
EP - 43
AN - OPUS4-63929
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 - 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 - JOUR
A1 - Ghata, Anupama
A1 - Bernges, Tim
A1 - Maus, Oliver
A1 - Wankmiller, Björn
A1 - Naik, Aakash
A1 - Bustamante, Joana
A1 - Gaultois, Michael W.
A1 - Delaire, Olivier
A1 - Hansen, Michael Ryan
A1 - George, Janine
A1 - Zeier, Wolfgang G.
T1 - Exploring the Thermal and Ionic Transport of Cu+ Conducting Argyrodite Cu7PSe6
N2 - AbstractUnderstanding the origin of low thermal conductivities in ionic conductors is essential for improving their thermoelectric efficiency, although accompanying high ionic conduction may present challenges for maintaining thermoelectric device integrity. This study investigates the thermal and ionic transport in Cu7PSe6, aiming to elucidate their fundamental origins and correlation with the structural and dynamic properties. Through a comprehensive approach including various characterization techniques and computational analyses, it is demonstrated that the low thermal conductivity in Cu7PSe6 arises from structural complexity, variations in bond strengths, and high lattice anharmonicity, leading to pronounced diffuson transport of heat and fast ionic conduction. It is found that upon increasing the temperature, the ionic conductivity increases significantly in Cu7PSe6, whereas the thermal conductivity remains nearly constant, revealing no direct correlation between ionic and thermal transport. This absence of direct influence suggests innovative design strategies in thermoelectric applications to enhance stability by diminishing ionic conduction, while maintaining low thermal conductivity, thereby linking the domains of solid‐state ionics and thermoelectrics. Thus, this study attempts to clarify the fundamental principles governing thermal and ionic transport in Cu+‐superionic conductors, similar to recent findings in Ag+ argyrodites.
KW - Thermoelectrics
KW - Phonons
KW - Chemically Complex Materials
KW - DFT
KW - Bonding Analysis
PY - 2024
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-608866
DO - https://doi.org/10.1002/aenm.202402039
SP - 1
EP - 9
PB - Wiley
AN - OPUS4-60886
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
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 - An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
KW - Bonding Analysis
KW - DFT
KW - High-throughput
KW - Database
KW - Phonons
KW - Machine Learning
PY - 2023
UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-582892
DO - https://doi.org/10.1038/s41597-023-02477-5
VL - 10
IS - 1
SP - 1
EP - 18
AN - OPUS4-58289
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 - GEN
A1 - George, Janine
A1 - Petretto, G.
A1 - Naik, Aakash
A1 - Esters, M.
A1 - Jackson, A. J.
A1 - Nelson, R.
A1 - Dronskowski, R.
A1 - Rignanese, G.-M.
A1 - Hautier, G.
T1 - Automated bonding analysis with crystal orbital Hamilton populations (program code LobsterPy)
N2 - This is the code for the program LobsterPy that can be used to automatically analyze and plot outputs of the program Lobster.
KW - Automation
KW - High-throughput computations
KW - Bonding analysis
PY - 2022
UR - https://doi.org/10.5281/zenodo.6320074
UR - https://doi.org/10.5281/zenodo.6415169
UR - https://doi.org/10.5281/zenodo.6415336
UR - https://doi.org/10.5281/zenodo.6581118
UR - https://doi.org/10.5281/zenodo.15034145
DO - https://doi.org/10.5281/zenodo.6320073
PB - Zenodo
CY - Geneva
AN - OPUS4-55174
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - George, Janine
A1 - Naik, Aakash
A1 - Jackson, A. J.
A1 - Baird, S.
T1 - Scripts to reproduce "Automated bonding analysis with crystal orbital Hamilton populations"
N2 - This repo allows to recreate our publication: https://doi.org/10.1002/cplu.202200123 In contrast to 0.2.2, we fixed an issue with absolute path.
KW - Automation
KW - High-throughput computations
KW - Bonding analysis
PY - 2022
UR - https://github.com/JaGeo/LobsterAutomation
UR - https://doi.org/10.5281/zenodo.6421928
UR - https://doi.org/10.5281/zenodo.6595062
UR - https://doi.org/10.5281/zenodo.6599556
UR - https://doi.org/10.5281/zenodo.6674670
UR - https://doi.org/10.5281/zenodo.6704163
DO - https://doi.org/10.5281/zenodo.6421927
PB - Zenodo
CY - Geneva
AN - OPUS4-55177
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - George, Janine
A1 - Naik, Aakash
T1 - DFT raw data for "Investigations on electron beam irradiated rare-earth doped SrF2 for application as low fading dosimeter material: Evidence for and DFT simulation of a radiation-induced phase"
N2 - DFT raw data for the publication https://doi.org/10.1039/D2TC01773C
It includes all predicted structures, the structure optimizations and the phonon computations in VASP format. Phonon computations were performed with the finite displacement method.
KW - DFT
KW - Phonons
KW - Structure prediction
PY - 2022
DO - https://doi.org/10.5281/zenodo.5942729
PB - Zenodo
CY - Geneva
AN - OPUS4-55417
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - JOUR
A1 - George, Janine
A1 - Petretto, G.
A1 - Naik, Aakash
A1 - Esters, M.
A1 - Jackson, A. J.
A1 - Nelson, R.
A1 - Dronskowski, R.
A1 - Rignanese, G.-M.
A1 - Hautier, G.
T1 - Cover profile for the article "Automated bonding analysis with crystal orbital Hamilton populuations"
N2 - Invited for this month’s cover are researchers from Bundesanstalt für Materialforschung und -prüfung (Federal Institute for Materials Research and Testing) in Germany, Friedrich Schiller University Jena, Université catholique de Louvain, University of Oregon, Science & Technology Facilities Council, RWTH Aachen University, Hoffmann Institute of Advanced Materials, and Dartmouth College. The cover picture shows a workflow for automatic bonding analysis with Python tools (green python). The bonding analysis itself is performed with the program LOBSTER (red lobster). The starting point is a crystal structure, and the results are automatic assessments of the bonding situation based on Crystal Orbital Hamilton Populations (COHP), including automatic plots and text outputs. Coordination environments and charges are also assessed. More information can be found in the Research Article by J. George, G. Hautier, and co-workers.
KW - Automation
KW - Chemical bonds
KW - High-throughput
PY - 2022
DO - https://doi.org/10.1002/cplu.202200246
SN - 2192-6506
SP - 1
EP - 2
PB - Wiley-VCH
CY - Weinheim
AN - OPUS4-55557
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - CONF
A1 - Naik, Aakash
T1 - New descriptors for materials properties based on bonding indicators
N2 - Includes a summary of the Ph.D. project that deals with generating a database populated with materials bonding properties and how we intend to gain deeper insights into material properties through this research.
T2 - SALSA 2022
CY - Berlin, Germany
DA - 15.09.2022
KW - Bonding analysis
KW - Automation
KW - Materials Informatics
KW - Computational Chemistry
PY - 2022
AN - OPUS4-56142
LA - eng
AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER -
TY - GEN
A1 - Pauw, Brian Richard
A1 - Laskina, Sofya
A1 - Naik, Aakash
A1 - Smales, Glen Jacob
A1 - George, Janine
A1 - Breßler, Ingo
A1 - Benner, Philipp
T1 - Jupyter notebook and VASP calculation details accompanying the manuscript: "Ultima Ratio: Simulating wide-range X-ray scattering and diffraction"
N2 - Summary:
This notebook and associated datasets (including VASP details) accompany a manuscript available on the ArXiv (https://doi.org/10.48550/arXiv.2303.13435) and hopefully soon in a journal as short communication as well. Most of the details needed to understand this notebook are explained in that paper with the same title as above. For convenience, the abstract is repeated here:
Paper abstract:
We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is presented coupled to the scattering intensity, may offer a novel pathway for determining the degree of crystallinity.
The "Ultima Ratio" strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from $Q<0.01$\,$\mathrm{nm}^{-1}$ up to $Q\approx150$\,$\mathrm{nm}^{-1}$, with a resolution of 0.16\,\AA. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to $8000^3$ voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-$Q$ behaviour.
The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder.
While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts.
KW - X-ray
KW - Simulation
KW - Scattering
KW - MOUSE
KW - Nanomaterials
KW - XRD
KW - SAXS
KW - PDF
KW - Total scattering
KW - 3D Fourier transform
KW - High resolution
KW - FFT
PY - 2023
DO - https://doi.org/10.5281/zenodo.7764044
PB - Zenodo
CY - Geneva
AN - OPUS4-57207
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 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 -
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 -