2018
DOI: 10.1103/physrevlett.120.156001
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Data-Driven Learning of Total and Local Energies in Elemental Boron

Abstract: 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 pot… Show more

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Cited by 192 publications
(185 citation statements)
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“…1 Overview of the GAP-RSS protocol as introduced in ref. 37 and extended here by a selection step. We start from a set of randomised seed structures for a given chemical composition (in generations 0-3, 250 each; in generation 4, 5000).…”
Section: Gap Ttingmentioning
confidence: 99%
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“…1 Overview of the GAP-RSS protocol as introduced in ref. 37 and extended here by a selection step. We start from a set of randomised seed structures for a given chemical composition (in generations 0-3, 250 each; in generation 4, 5000).…”
Section: Gap Ttingmentioning
confidence: 99%
“…found to be suitable for GAP-RSS in ref. 37. For a more detailed walk-through of the underlying ML framework, we refer the reader to ref.…”
Section: Gap Ttingmentioning
confidence: 99%
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