TY - CONF A1 - George, Janine T1 - Lobster workflow and applications N2 - This talk introduced bonding analysis to the participants of the school including several examples of its usefulness. Additionally, I talked in detail about the workflows related to bonding analysis within the atomate2 workflow library. I then showed how the workflow was applied to build a very large bonding data database that can now be used for machine learning of materials properties. T2 - "Automated ab initio workflows with Jobflow and Atomate2" CECAM Flagship school CY - Lausanne, Switzerland DA - 17.03.2025 KW - Automation KW - Materials design KW - Machine learning KW - Thermal conductivity KW - Inorganic materials KW - High-throughput PY - 2025 AN - OPUS4-62948 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Phonon workflow and applications N2 - This talk introduced the participants to phonons and how they are typically computed. Then, I introduced the participants to the harmonic phonon, the Grüneisen, and the quasi-harmonic workflow that allows the computation of properties related to phonons. I also had detailed examples of how these workflows can be used in practice. Beyond this, I showed how these workflows can be used to automatically benchmark interatomic potentials and use them for the development of such potentials. T2 - "Automated ab initio workflows with Jobflow and Atomate2" CECAM Flagship school CY - Lausanne, Switzerland DA - 17.03.2025 KW - Automation KW - Phonons KW - Thermal Conductivity KW - Machine Learning KW - Software Development KW - Machine Learned interatomic Potentials PY - 2025 AN - OPUS4-62949 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-Driven Materials Design N2 - Implementations and algorithms, such as ChemEnv and LobsterEnv, for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are now available. Fully automatic workflows and analysis tools have been developed to use quantumchemical bonding analysis on a large scale. The first part of the lecture will demonstrate how our tools, which assess local atomic environments and perform automatic bonding analysis, help develop new machine-learning models and a new intuitive understanding of material properties. Many new universal machine-learned interatomic potentials, such as MACE-MP-0, have been developed. The second part of the lecture will showcase how these potentials, in combination with DFT, could significantly accelerate our research. The focus will be on the interplay of DFT and machine-learned interatomic potentials, presenting new fully automated workflows for training, finetuning, and implemented benchmarking these potentials, in our software autoplex (https://github.com/autoatml/autoplex). Additionally, I will show how to train new interatomic potentials from scratch by exploring potential energy surfaces extensively, offering a method to enhance current universal machine-learned potentials. Beyond this, the general trend toward automation in computational materials science and some of our recent contributions will be discussed. T2 - AI4AM2025 CY - Donostia, Spain DA - 08.04.2025 KW - Automation KW - Machine Learning KW - Chemically Complex Materials KW - Thermal Conductivity KW - Bonding Analysis KW - Workflows KW - Machine Learned Interatomic Potentials PY - 2025 AN - OPUS4-62950 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 - GEN A1 - Janssen, Jan A1 - George, Janine A1 - Geiger, Julian A1 - Bercx, Marnik A1 - Wang, Xing A1 - Ertural, Christina A1 - Schaarschmidt, Joerg A1 - Ganose, Alex M. A1 - Pizzi, Giovanni A1 - Hickel, Tilmann A1 - Neugebauer, Joerg T1 - A Python workflow definition for computational materials design N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA, jobflow, and pyiron. KW - Automation KW - Workflows KW - Materials Design KW - Multi-scale simulation KW - Digitalization PY - 2025 DO - https://doi.org/10.5281/zenodo.15516179 PB - Zenodo CY - Geneva AN - OPUS4-63233 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Janssen, Jan A1 - George, Janine A1 - Geiger, Julian A1 - Bercx, Marnik A1 - Wang, Xing A1 - Ertural, Christina A1 - Schaarschmidt, Joerg A1 - Ganose, Alex M. A1 - Pizzi, Giovanni A1 - Hickel, Tilmann A1 - Neugebauer, Joerg T1 - A Python workflow definition for computational materials design N2 - Numerous Workflow Management Systems (WfMS) have been developed in the field of computational materials science with different workflow formats, hindering interoperability and reproducibility of workflows in the field. To address this challenge, we introduce here the Python Workflow Definition (PWD) as a workflow exchange format to share workflows between Python-based WfMS, currently AiiDA, jobflow, and pyiron. This development is motivated by the similarity of these three Python-based WfMS, that represent the different workflow steps and data transferred between them as nodes and edges in a graph. With the PWD, we aim at fostering the interoperability and reproducibility between the different WfMS in the context of Findable, Accessible, Interoperable, Reusable (FAIR) workflows. To separate the scientific from the technical complexity, the PWD consists of three components: (1) a conda environment that specifies the software dependencies, (2) a Python module that contains the Python functions represented as nodes in the workflow graph, and (3) a workflow graph stored in the JavaScript Object Notation (JSON). The first version of the PWD supports directed acyclic graph (DAG)-based workflows. Thus, any DAG-based workflow defined in one of the three WfMS can be exported to the PWD and afterwards imported from the PWD to one of the other WfMS. After the import, the input parameters of the workflow can be adjusted and computing resources can be assigned to the workflow, before it is executed with the selected WfMS. This import from and export to the PWD is enabled by the PWD Python library that implements the PWD in AiiDA, jobflow, and pyiron. KW - Automation KW - Workflow KW - Materials Design KW - Multi-scale simulation KW - Digitalization PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-632328 DO - https://doi.org/10.48550/arXiv.2505.20366 SP - 1 EP - 12 AN - OPUS4-63232 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 - Rosen, Andrew S. A1 - Gallant, Max A1 - George, Janine A1 - Riebesell, Janosh A1 - Sahasrabuddhe, Hrushikesh A1 - Shen, Jimmy-Xuan A1 - Wen, Mingjian A1 - Evans, Matthew L. A1 - Petretto, Guido A1 - Waroquiers, David A1 - Rignanese, Gian-Marco A1 - Persson, Kristin A. A1 - Jain, Anubhav A1 - Ganose, Alex M. T1 - Jobflow: Computational Workflows Made Simple N2 - Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager. Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases. KW - Automation KW - Workflows KW - Computational Materials Science PY - 2024 DO - https://doi.org/10.5281/zenodo.10466868 PB - Zenodo CY - Geneva AN - OPUS4-59313 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hodoroaba, Vasile-Dan T1 - Correlative physic-chemical analysis of advanced materials and BAM’s green and zero-related advanced materials activities – Benefits and challenges by examples N2 - Following points are presented: - Nanomaterials BAM, as research materials & CRMs, incl pre-standardization, automated analysis & automated synthesis - 2D-materials BAM HEurope ACCORDs approach Green Deal inspired correlative imaging based characterisation for safety profiling of 2D materials - Sample preparation @BAM , inclusion to standards - Thin Films @BAM as Reference Material Candidates, i) mesoporous, ii) HEA - Substitutional,Transition Me oxides as electrocatalysts a project @BAM - Sustainable mechanochemical reaction engineering projects @BAM. T2 - VAMAS Workshop Advanced Materials Challenges and Standardization Needs for a Greener World CY - Rio de Janeiro, Brazil DA - 25.09.2024 KW - Nanomaterials KW - Advanced materials KW - 2D materials KW - Sample preparation KW - Thin films KW - Reference materials KW - Safety KW - Automation PY - 2024 AN - OPUS4-61404 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -