TY - CONF A1 - Ertural, Christina T1 - Workflows and automation (lecture/tutorial) N2 - A lecture and tutorial on how to use workflows and automation methods for quantum chemical calculations. T2 - Invited lecture CY - Oxford, UK DA - 28.04.2023 KW - Workflows KW - Automation KW - Quantum chemical bonding analysis PY - 2023 AN - OPUS4-57934 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 - VIDEO A1 - George, Janine T1 - Materials design using chemical heuristics, workflows, and machine learning N2 - Bonds and local atomic environments are key descriptors of material properties, used to establish design rules and heuristics, and serve as descriptors in machine-learned interatomic potentials and the general machine learning of material properties. Software implementations such as ChemEnv and LobsterEnv identify local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis (here using Crystal Orbital Hamilton Populations as computed with LOBSTER). Fully automated workflows and analysis tools now enable large-scale quantum-chemical bonding analysis. The first part of the lecture will demonstrate how these tools help develop new machine-learning models and intuitive understandings of material properties. 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, combined with DFT, can accelerate research. It will focus on the interplay between DFT and machine-learned interatomic potentials, presenting automated workflows for training, fine-tuning, and benchmarking these potentials, implemented in our software autoplex. Additionally, it will show how to train new interatomic potentials from scratch by exploring potential energy surfaces, with the potential to enhance current universal machine-learned potentials. The lecture will also discuss the trend toward automation in computational materials science and our recent contributions. T2 - FAIRmat Seminar CY - Berlin, Germany DA - 05.06.2025 KW - Materials Design KW - Machine Learning KW - Chemical Bonding KW - Batteries KW - Amorphous Materials KW - Workflows KW - Machine Learned Interatomic Potentials KW - Phonons KW - Magnetism KW - Synthesizability PY - 2025 UR - https://www.youtube.com/watch?v=Sfco48s1fpU PB - YouTube, LLC CY - San Bruno, CA, USA AN - OPUS4-63744 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Automation and Workflows in Computational Materials Science N2 - This talks describes why we need automation and workflows in materials informatics. It introduces tools to automatize tasks in computational materials science. Furthermore, a bonding analysis and a phonon workflow are presented. T2 - Seminar in Theoretical Chemistry Group atFU Berlin CY - Berlin, Germany DA - 06.12.2022 KW - Automation KW - Workflows KW - Density functional theory PY - 2022 AN - OPUS4-56524 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Automation and workflows incomputational materials science N2 - This talk introduced the audience to automation and workflows in the field of computational materials science. The audience were the members of the FONDA Sonderforschungsbereich at HU Berlin T2 - FONDA Seminar Series CY - Berlin, Germany DA - 10.10.2022 KW - Automation KW - Workflows KW - Materials Informatics PY - 2022 AN - OPUS4-56419 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Automated quantum-chemical bonding analysis with workflow tools N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, other recent workflow contributions to the Materials Project software infrastructure (pymatgen, atomate2) related to phonons and machine-learning potentials will be discussed. T2 - ADIS Workshop 2023 CY - Tegernsee, Germany DA - 29.10.2023 KW - Automation KW - Materials Properties KW - DFT KW - Workflows KW - Machine Learning KW - Thermodynamic Properties PY - 2023 AN - OPUS4-58745 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - New Opportunities for Data-Driven Chemistry and Materials Science Through Automation N2 - In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based). This ranges from the automation of simple heuristics (oxidation states, coordination environments) to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities. For example, we can now easily make data-driven tests of well-known rules and heuristics or develop quantum chemistry-based materials descriptors for machine learning approaches. These tests and descriptors can have applications related to magnetic ground state predictions of materials relevant for spintronic applications or for predicting thermal properties relevant for thermal management in electronics. Combining high-throughput ab initio computations with fitting, fine-tuning machine learning models and predictions of such models within complex workflows is also possible and promises further acceleration in the field. In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science. T2 - MRS Spring CY - Seattle, Washington, USA DA - 21.04.2024 KW - Automation KW - Workflows KW - Chemical Bonding KW - Thermal Properties KW - Materials Design PY - 2024 AN - OPUS4-59982 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-throughput and automated bonding Analysis N2 - Talk as a part of the LOBSTER CECAM SCHOOL. This talk introduced all participants to automation tools around the software LOBSTER. T2 - CECAM LOBSTER School CY - Aalto, Finland DA - 12.03.2024 KW - Automation KW - Workflows KW - Bonding Analysis KW - Materials Design KW - Chemically Complex Materials PY - 2024 AN - OPUS4-59672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-throughput approaches for materials understanding and design N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties.[1] Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available.[2,3] Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale.[3,4] The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.[7–11] T2 - TCO 2024 - Transparent Conductive Oxides, Fundamentals and Applications CY - Leipzig, Germany DA - 23.09.2024 KW - Automation KW - Materials discovery KW - Machine Learned Interatomic Potentials KW - Workflows KW - Chemically Complex Materials KW - Bonding Analysis PY - 2024 AN - OPUS4-61153 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - New opportunities through an efficient combination of machine learning and high-throughput computing N2 - The underlying data is crucial for machine learning (ML) tasks.[1] Ab initio data is often used as a target (occasionally as features[2]). High-throughput calculations and automation make it possible to generate such data as efficiently as possible and with uniform standards.[3,4] The high-throughput data of the Materials Project has recently been used to train new foundation interatomic potentials[5]. Several high-throughput frameworks have been developed in recent years. This presentation will introduce the atomate2/jobflow[3,4] framework that is used and developed by researchers around the Materials Project. In the future, it will be used to generate the data for the Materials Project. Atomate2 allows both the use of DFT and ML potentials within one framework. Building upon this framework, we have developed a package with the possibility to train and benchmark ML potentials automatically. Currently, the package can be used to train ML interatomic potentials sufficient to predict harmonic phononic properties of materials. Typical foundation models in this area now achieve acceptable results but are still far from routinely replacing DFT.[5,6] Building on the promising results from the ref where we explored data generation strategies for accurate phononic properties[7], we will present new fully automated workflows for training and benchmarking ML interatomic potentials with force predictions that are accurate enough to compute harmonic phonons in very good agreement with DFT for different crystal structures of the same composition. T2 - GAP/(M)ACE Developers & Users Meeting 2024 CY - Berlin, Germany DA - 17.09.2024 KW - Automation KW - Workflows KW - Machine Learned Interatomic Potentials KW - Thermal Properties KW - Bonding Analysis KW - Chemically Complex Materials PY - 2024 AN - OPUS4-61099 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine ED - Bastian, P. ED - Dranzlmüller, D. ED - Brüchle, H. ED - Mathias, G. T1 - Accelerated Materials Discovery with Automation and Machine­Learned Chemical Knowledge N2 - This project aims to accelerate the search for new materials (e.g., for thermoelectric applications, battery materials, magnets, and other materials classes) based on ab initio high­throughput studies. High­throughput searches are typically restricted to known materials. This project explores strategies (data­driven chemical heuristics in subproject 1 and machine­learned interatomic potentials in subproject 2) to go beyond current database entries and include such computationally demanding properties in high­throughput searches. To accomplish each subproject, we develop automated workflows for high­throughput computations and provide large open databases of computed materials properties to the research community. KW - Automation KW - Chemically Complex Materials KW - Machine Learning KW - Machine-Learned Interatomic Potentials KW - Workflows PY - 2024 UR - https://doku.lrz.de/files/10745976/1136429625/1/1731329162337/2024_SuperMUC-Results-Reports.pdf SN - 978-3-9816675-6-1 SP - 72 EP - 75 AN - OPUS4-61618 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine T1 - Materialinformatik N2 - Die Materialinformatik ermöglicht es, vorhandenes/etabliertes chemisches Wissen zu überprüfen, aber auch neues Wissen zu generieren. Zu diesem Zweck wurden automatisierte Methoden entwickelt, die es ermöglichen, große Datenbanken mit Materialeigenschaften zu berechnen. Diese Datenbanken mit berechneten Eigenschaften können nun mit Datenanalysetechniken und maschinellem Lernen ausgewertet werden. Unsere Entwicklungen ermöglichen es insbesondere, die Analyse der chemischen Bindung mit anderen Materialeigenschaften in großem Maßstab zu kombinieren. Auf der Grundlage von Deskriptoren für chemische Bindungen haben wir die ersten Schritte zur Entwicklung neuer chemischer Regeln unternommen. Daneben entwickeln wir weitere Programmcodes, die die Tätigkeiten in der Materialinformatik deutlich erleichtern KW - Materialdesign KW - Maschinelles Lernen KW - Automatisierung KW - Hochdurchsatz KW - Workflows KW - Materialeigenschaften PY - 2024 SN - 978-3-8007-6472-3 SP - 14 EP - 32 CY - Berlin AN - OPUS4-62256 LA - deu 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 - CONF A1 - Hickel, Tilmann T1 - Interoperable workflows combining software tools along the process chain of materials N2 - Material science problems have intrinsically multiscale and multiphysics characters, and require us to employ a combination of methods on different time and length scales to resolve critical features. Normally creating workflows that connect data in multiple scales and various methods is a cumbersome task. Pyiron, an integrated development environment (IDE) for material science, contains modules for the atomistic as well as continuum scale that make a seamless connection possible. To this end, it provides a high-level coherent language in a unified workflow platform to study materials, for example, with LAMMPS simulations in the same framework as crystal plasticity codes such as DAMASK. The latter is integrated such that all required input files can be generated, the analysis can be executed and the routines of the DAMASK post-processing library can be employed. One example of a multi-scale workflow is the simulation of a multi-stage cold rolling process using pyiron. As with every DAMASK simulation, pre-processing includes the creation of the representative volume element and the material definition. It contains elastic properties that are obtained from LAMMPS calculations. To simulate the actual rolling process, the rolling subclass was implemented in the pyiron-job class. Since the total height decrease in technical rolling processes can be very large, the implementation provides for optional regridding between rolling passes. This is also required for the coupling with OpenPhase for simulating recrystallization using pyiron. To ensure interoperability between evaluation and simulation tools, work was done on a semantic representation of pyiron workflows. T2 - MSE Conference 2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Steel KW - Workflows KW - Multiscale simulation KW - Ontology PY - 2024 AN - OPUS4-62728 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 -