An automated framework for exploring and learning potential-energy surfaces

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 d...

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Hauptverfasser:	Liu, Yuanbin, Morrow, Joe D, Ertural, Christina, Fragapane, Natascia L, Gardner, John L. A, Naik, Aakash A, Zhou, Yuxing, George, Janine, Deringer, Volker L
Format:	Artikel
Sprache:	eng
Schlagworte:	Physics - Computational Physics Physics - Materials Science
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