TY - JOUR A1 - Horton, Matthew K. A1 - Huck, Patrick A1 - Yang, Ruo Xi A1 - Munro, Jason M. A1 - Dwaraknath, Shyam A1 - Ganose, Alex M. A1 - Kingsbury, Ryan S. A1 - Wen, Mingjian A1 - Shen, Jimmy X. A1 - Mathis, Tyler S. A1 - Kaplan, Aaron D. A1 - Berket, Karlo A1 - Riebesell, Janosh A1 - George, Janine A1 - Rosen, Andrew S. A1 - Spotte-Smith, Evan W. C. A1 - McDermott, Matthew J. A1 - Cohen, Orion A. A1 - Dunn, Alex A1 - Kuner, Matthew C. A1 - Rignanese, Gian-Marco A1 - Petretto, Guido A1 - Waroquiers, David A1 - Griffin, Sinead M. A1 - Neaton, Jeffrey B. A1 - Chrzan, Daryl C. A1 - Asta, Mark A1 - Hautier, Geoffroy A1 - Cholia, Shreyas A1 - Ceder, Gerbrand A1 - Ong, Shyue Ping A1 - Jain, Anubhav A1 - Persson, Kristin A. T1 - Accelerated data-driven materials science with the Materials Project N2 - The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational Methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our eforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community. KW - Databases KW - Materials Informatics KW - Materials Design KW - Automation KW - Workflows KW - Accelerated Discovery PY - 2025 DO - https://doi.org/10.1038/s41563-025-02272-0 SN - 1476-1122 SP - 1 EP - 11 PB - Springer Science and Business Media LLC AN - OPUS4-63616 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 - JOUR A1 - Ghiringhelli, Luca M. A1 - Baldauf, Carsten A1 - Bereau, Tristan A1 - Brockhauser, Sandor A1 - Carbogno, Christian A1 - Chamanara, Javad A1 - Cozzini, Stefano A1 - Curtarolo, Stefano A1 - Draxl, Claudia A1 - Dwaraknath, Shyam A1 - Fekete, Ádám A1 - Kermode, James A1 - Koch, Christoph T. A1 - Kühbach, Markus A1 - Ladines, Alvin Noe A1 - Lambrix, Patrick A1 - Himmer, Maja-Olivia A1 - Levchenko, Sergey V. A1 - Oliveira, Micael A1 - Michalchuk, Adam A1 - Miller, Ronald E. A1 - Onat, Berk A1 - Pavone, Pasquale A1 - Pizzi, Giovanni A1 - Regler, Benjamin A1 - Rignanese, Gian-Marco A1 - Schaarschmidt, Jörg A1 - Scheidgen, Markus A1 - Schneidewind, Astrid A1 - Sheveleva, Tatyana A1 - Su, Chuanxun A1 - Usvyat, Denis A1 - Valsson, Omar A1 - Wöll, Christof A1 - Scheffler, Matthias T1 - Shared metadata for data-centric materials science N2 - The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science”. We start from an operative definition of metadata, and the features that a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them. KW - Library and Information Sciences KW - Statistics, Probability and Uncertainty KW - Computer Science Applications KW - Education KW - Information Systems KW - Statistics and Probability PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-584846 DO - https://doi.org/10.1038/s41597-023-02501-8 VL - 10 IS - 1 SP - 1 EP - 18 PB - Springer Science and Business Media LLC AN - OPUS4-58484 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -