Data-Driven Materials Design

  • 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,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.zeige mehrzeige weniger

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Metadaten
Autor*innen:Janine GeorgeORCiD
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:6 Materialchemie
6 Materialchemie / 6.0 Abteilungsleitung und andere
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:Automation; Bonding Analysis; Chemically Complex Materials; Machine Learned Interatomic Potentials; Machine Learning; Thermal Conductivity; Workflows
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Materialdesign
Veranstaltung:AI4AM2025
Veranstaltungsort:Donostia, Spain
Beginndatum der Veranstaltung:08.04.2025
Enddatum der Veranstaltung:10.04.2025
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:15.04.2025
Referierte Publikation:Nein
Eingeladener Vortrag (wissenschaftliche Konferenzen):Ja
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