Materials design using chemical heuristics, workflows, and machine learning

  • 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 interplayBonds 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.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; High-throughput; Machine learning; Materials acceleration platforms; Sustainable materials design; Synthesizability
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Materialdesign
Veranstaltung:FAIRmat Seminar
Veranstaltungsort:Berlin, Germany
Beginndatum der Veranstaltung:05.06.2025
Zugehöriger Identifikator:https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/63744
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:10.06.2025
Referierte Publikation:Nein
Eingeladener Vortrag (wissenschaftliche Konferenzen):Ja
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