Synthetic pre-training for neural-network interatomic potentials

Machine learning (ML) based interatomic potentials have transformed the field of atomistic materials modelling. However, ML potentials depend critically on the quality and quantity of quantum-mechanical reference data with which they are trained, and therefore developing datasets and training pipeli...

詳細記述

書誌詳細

主要な著者:	John L A Gardner, Kathryn T Baker, Volker L Deringer
フォーマット:	Artigo
言語:	Inglês
出版事項:	IOP Publishing 2024-01-01
シリーズ:	Machine Learning: Science and Technology
主題:	machine learning neural networks synthetic data atomistic simulations molecular dynamics
オンライン･アクセス:	https://doi.org/10.1088/2632-2153/ad1626
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