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 -