Data-Driven Learning of Total and Local Energies in Elemental Boron.

By Volker L. Deringer, Chris J. Pickard, Gábor Csányi. 2017

153 citations recorded by the source corpus.

The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery.

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Citations in the corpus

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields (2022, background): “Models that are signiﬁcantly faster to train are better suited for applications of active learning, which is typically how databases for materials science applications are built [13–15].”
Machine Learning a General-Purpose Interatomic Potential for Silicon (2018, methodology): “This representation was later simplified to the SOAP representation and kernel [7] and applied to tungsten [67], amorphous carbon [11,68], iron [69], graphene [12], and boron [70].”
Machine-Learning Interatomic Potentials for Materials Science (2021, background): “GAP potentials have provided new insights into the energetics and structures of the numerous hypothetical polymorphs of carbon, boron, and phosphorous [92, 101, 122, 139, 140, 148].”