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 - JOUR A1 - Nowatzky, Yannek A1 - Benner, Philipp A1 - Reinert, K. A1 - Muth, Thilo T1 - Mistle: bringing spectral library predictions to metaproteomics with an efficient search index N2 - Motivation: Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics. Results: In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step. We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes. Availability and implementation: Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle. KW - Mass spectrometry KW - Proteomics KW - Algorithms KW - Metaproteomics PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-579373 DO - https://doi.org/10.1093/bioinformatics/btad376 SN - 1367-4811 VL - 39 IS - 6 SP - 1 EP - 12 PB - Oxford University Press CY - Oxford, Great Britain AN - OPUS4-57937 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Amariamir, Sasan A1 - Benner, Philipp A1 - George, Janine T1 - Prediction of materials synthesizability N2 - In the pursuit of discovering materials with desirable properties, extending the available material libraries is crucial. High-throughput simulations have become an integral part in designing new materials in the past decades. However, there is no straightforward way of distinguishing synthesizable materials from all the proposed candidates. This project focuses on employing AI-driven methods to estimate synthesizability of materials. Up to now, material scientists and engineers have relied on domain knowledge as well as empirical heuristics to guess the stability and synthesizability of molecules and crystals. The famous Pauling rules of crystal stability are an example of such heuristics. However, after the accelerating material discovery in all the years since Pauling, these rules now fail to account for the stability of most known crystals. A new predictive set of heuristics for crystal stability/synthesizability is unlikely to be uncovered by human perception, given the magnitude and dimensionality of crystallographic data. Hence, a data-driven approach should be proposed to find a predictive model or set of heuristics which differentiate synthesizable crystal structures from the rest. The main challenge of this research problem is the lack of a negative set for classification. Here, there are two classes of data: the positive class which contains synthesizable materials and the negative class which contains materials which are not synthesizable. While the data from the positive class is simply the data of crystals which have been experimentally synthesized, we do not have access to data points which are certainly unsynthesizable. Strictly speaking, if an attempt of synthesizing a crystal fails, it does not necessarily follow that the crystal is not synthesizable. Also, there is no database available which contains the intended crystal structures of unsuccessful synthesis attempts. This project proposes a semi-supervised learning scheme to predict crystal synthesizability. The ML model is trained on experimental and theoretical crystal data. The initial featurization focuses on local environments which is inspired by the Pauling Rules. The experimental data points are downloaded through the Pymatgen API from the Materials Project database which contains relaxed structures recorded in Inorganic Crystal Structure Database – ICSD. The theoretical data is queried from select databases accessible through the Optimade project’s API. T2 - MSE Congress 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Synthesizability KW - PU Learning KW - Cheminformatics PY - 2022 AN - OPUS4-56731 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Benner, Philipp T1 - eScience bei Produktprüfung und Zertifizierung N2 - Das eScience Referat unterstützt die Forschungsbereiche der BAM im Bereich Research Data Management und Data Science. Der Vortrag gibt beispielhafte Einblicke in die Aktivitäten der eScience. Insbesondere wird das Projekt QI-Digital vorgestellt, welches als Ziel die Bereitstellung einer digitalen Qualitätsinfrastruktur hat. T2 - EDAP CY - Online meeting DA - 03.11.2021 KW - Produktprüfung KW - Zertifizierung PY - 2021 AN - OPUS4-56692 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Albakri, Bashar A1 - Turski Silva Diniz, Analice A1 - Benner, Philipp A1 - Muth, Thilo A1 - Nakajima, Shinichi A1 - Favaro, Marco A1 - Kister, Alexander ED - Hillman, Robert T1 - Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers N2 - Polymers have become indispensable across fields of application, and understanding their structure–property relationships and dynamic behaviour is essential for performance optimization. Polymer membranes, particularly ion exchange membranes, play a crucial role in renewable energy conversion technologies, fuel cells, solar energy conversion, and energy storage. In this context, broadband dielectric spectroscopy (BDS) offers a powerful, non-destructive approach to investigate the electrical response and relaxation dynamics of polymers. These properties are investigated by parametrizing the system’s impedance response in terms of a network of circuit elements, i.e. the electrical equivalent circuit (EEC), whose impedance resembles the one of the system under investigation. However, the determination of the EEC from BDS data is challenging due to system complexity, interdependencies of circuit elements, and researcher biases. In this work, we propose a novel approach that incorporates a convolutional neural network (CNN) model to predict the EEC topology. By reducing user bias and enhancing data analysis, this approach aims to make BDS accessible to both experienced users and those with limited expertise. We show that the combination of machine learning and BDS provides valuable insights into the dynamic behaviour of polymer membranes, thus facilitating the design and characterization of tailored polymers for various applications. We also show that our model outperforms state-of-the-art machine learning methods with a top-5 accuracy of around 80% for predicting the circuit topology and a parameter fitting error as low as 0.05%. KW - Polymer membranes KW - Electrochemical impedance spectroscopy KW - Broadband dielectric spectroscopy KW - Deep learning KW - Machine learning KW - Equivalent circuit PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602138 DO - https://doi.org/10.1016/j.electacta.2024.144474 SN - 0013-4686 VL - 496 SP - 1 EP - 13 PB - Elsevier Ltd. AN - OPUS4-60213 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Maack, Stefan A1 - Benner, Philipp A1 - Kröll, Mirco A1 - Prager, Jens A1 - Daum, Werner A1 - Casperson, Ralf A1 - Heckel, Thomas A1 - Spaltmann, Dirk A1 - et al., ED - Wahlster, W. ED - Winterhalter, C. T1 - Deutsche Normungsroadmap künstliche Intelligenz T1 - German standardization roadmap on artificial intelligence N2 - Im Auftrag des Bundesministeriums für Wirtschaft und Klimaschutz haben DIN und DKE im Januar 2022 die Arbeiten an der zweiten Ausgabe der Deutschen Normungsroadmap Künstliche Intelligenz gestartet. In einem breiten Beteiligungsprozess und unter Mitwirkung von mehr als 570 Fachleuten aus Wirtschaft, Wissenschaft, öffentlicher Hand und Zivilgesellschaft wurde damit der strategische Fahrplan für die KI-Normung weiterentwickelt. Koordiniert und begleitet wurden diese Arbeiten von einer hochrangigen Koordinierungsgruppe für KI-Normung und -Konformität. Mit der Normungsroadmap wird eine Maßnahme der KI-Strategie der Bundesregierung umgesetzt und damit ein wesentlicher Beitrag zur „KI – Made in Germany“ geleistet. Die Normung ist Teil der KI-Strategie und ein strategisches Instrument zur Stärkung der Innovations- und Wettbewerbsfähigkeit der deutschen und europäischen Wirtschaft. Nicht zuletzt deshalb spielt sie im geplanten europäischen Rechtsrahmen für KI, dem Artificial Intelligence Act, eine besondere Rolle. N2 - On behalf of the German Federal Ministry of Economic Affairs and Climate Action, DIN and DKE started work on the second edition of the German Standardization Roadmap Artificial Intelligence in January 2022. With the broad participation and involvement of more than 570 experts from industry, science, the public sector and civil society, the strategic Roadmap for AI standardization was thus further developed. This work was coordinated and accompanied by a high-level coordination group for AI standardization and conformity. The standardization roadmap implements a measure of the German government’s AI Strategy and thus makes a significant contribution to “AI – Made in Germany”. Standardization is part of the AI Strategy and is a strategic instrument for strengthening the innovation and competitiveness of the German and European economies. Not least for this reason, standardization plays a special role in the planned European legal framework for AI, the Artificial Intelligence Act. KW - Digitalisierung KW - Künstliche Intelligenz KW - Datenwissenschaft KW - Normung Roadmap KW - Data science KW - Artificial intelligence KW - Standardization roadmap KW - Digitization PY - 2022 UR - https://www.din.de/resource/blob/1072988/20bf33d405710a703aa26f81362493bb/ausgabe-2-nrm-ki-data.pdf UR - https://www.dke.de/de/arbeitsfelder/core-safety/normungsroadmap-ki SP - 1 EP - 448 PB - Deutsches Institut für Normung (DIN) CY - Berlin ET - 2. AN - OPUS4-56576 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Riebesell, Janosh A1 - Goodall, Rhys E. A. A1 - Benner, Philipp A1 - Chiang, Yuan A1 - Deng, Bowen A1 - Ceder, Gerbrand A1 - Asta, Mark A1 - Lee, Alpha A. A1 - Jain, Anubhav A1 - Persson, Kristin A. T1 - A framework to evaluate machine learning crystal stability predictions N2 - The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for machine learning energy models, here applied as pre-filters to first-principles computed data in a high-throughput search for stable inorganic crystals. We address the disconnect between (1) thermodynamic stability and formation energy and (2) retrospective and prospective benchmarking for materials discovery. Alongside this paper, we publish a Python package to aid with future model submissions and a growing online leaderboard with adaptive user-defined weighting of various performance metrics allowing researchers to prioritize the metrics they value most. To answer the question of which machine learning methodology performs best at materials discovery, our initial release includes random forests, graph neural networks, one-shot predictors, iterative Bayesian optimizers and universal interatomic potentials. We highlight a misalignment between commonly used regression metrics and more task-relevant classification metrics for materials discovery. Accurate regressors are susceptible to unexpectedly high false-positive rates if those accurate predictions lie close to the decision boundary at 0 eV per atom above the convex hull. The benchmark results demonstrate that universal interatomic potentials have advanced sufficiently to effectively and cheaply pre-screen thermodynamic stable hypothetical materials in future expansions of high-throughput materials databases. KW - Machine Learning KW - Interatomic Potentials PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-637139 DO - https://doi.org/10.1038/s42256-025-01055-1 SN - 2522-5839 VL - 7 IS - 6 SP - 836 EP - 847 PB - Springer Science and Business Media LLC AN - OPUS4-63713 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Amariamir, Sasan A1 - George, Janine A1 - Benner, Philipp T1 - SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction N2 - Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materials, remains a complex challenge due to the limitations of traditional heuristics and thermodynamic proxies. While stability metrics such as formation energy offer partial insights, they fail to account for kinetic factors and technological constraints that influence synthesis outcomes. These challenges are further compounded by the scarcity of negative data, as failed synthesis attempts are often unpublished or context-specific. We present SynCoTrain, a semi-supervised machine learning model designed to predict the synthesizability of materials. SynCoTrain employs a co-training framework leveraging two complementary graph convolutional neural networks: SchNet and ALIGNN. By iteratively exchanging predictions between classifiers, SynCoTrain mitigates model bias and enhances generalizability. Our approach uses Positive and Unlabeled (PU) learning to address the absence of explicit negative data, iteratively refining predictions through collaborative learning. The model demonstrates robust performance, achieving high recall on internal and leave-out test sets. By focusing on oxide crystals, a well-characterized material family with extensive experimental data, we establish SynCoTrain as a reliable tool for predicting synthesizability while balancing dataset variability and computational efficiency. This work highlights the potential of co-training to advance high-throughput materials discovery and generative research, offering a scalable solution to the challenge of synthesizability prediction. KW - Synthesizability KW - Accelerated Materials Design KW - Materials Acceleration Platforms KW - Materials Design KW - Materials Discovery KW - Co-training PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-637907 DO - https://doi.org/10.1039/D4DD00394B SN - 2635-098X VL - 4 IS - 6 SP - 1437 EP - 1448 PB - Royal Society of Chemistry (RSC) AN - OPUS4-63790 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 - Amariamir, Sasan A1 - George, Janine A1 - Benner, Philipp T1 - SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction N2 - Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materials, remains a complex challenge due to the limitations of traditional heuristics and thermodynamic proxies. While stability metrics such as formation energy offer partial insights, they fail to account for kinetic factors and technological constraints that influence synthesis outcomes. These challenges are further compounded by the scarcity of negative data, as failed synthesis attempts are often unpublished or context-specific. We present SynCoTrain, a semi-supervised machine learning model designed to predict the synthesizability of materials. SynCoTrain employs a co-training framework leveraging two complementary graph convolutional neural networks: SchNet and ALIGNN. By iteratively exchanging predictions between classifiers, SynCoTrain mitigates model bias and enhances generalizability. Our approach uses Positive and Unlabeled (PU) Learning to address the absence of explicit negative data, iteratively refining predictions through collaborative learning. The model demonstrates robust performance, achieving high recall on internal and leave-out test sets. By focusing on oxide crystals, a well-characterized material family with extensive experimental data, we establish SynCoTrain as a reliable tool for predicting synthesizability while balancing dataset variability and computational efficiency. This work highlights the potential of co-training to advance high-throughput materials discovery and generative research, offering a scalable solution to the challenge of synthesizability prediction. KW - Materials Design KW - Materials Discovery KW - Synthesizability KW - Machine Learning KW - Cotraining KW - New Materials KW - Materials Acceleration Platforms PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622104 DO - https://doi.org/10.48550/arXiv.2411.12011 SP - 1 EP - 39 AN - OPUS4-62210 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - 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 - 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 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 - Benner, Philipp A1 - Grandel, Jonas T1 - A shortcut towards phonon predictions N2 - Phonon calculations with ab-initio methods are computationally expensive. The use of universal machine learning models reduces the cost, but raises concerns about prediction quality. Fine-tuning with only a few structures, improves predictions of phonons, thermal properties and especially diffusive thermal conductivity, while reducing computational cost by a factor of 10 in average compared to DFT methods. T2 - DPG Frühjahrstagung CY - Regensburg, Germany DA - 16.03.2025 KW - Machine Learning KW - MACE KW - Phonons PY - 2025 AN - OPUS4-62770 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -