On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

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Machine learning (ML) is used to derive local stability information for density functional theory calculations of systems in relation to the recently discovered SnO2(110)-(4x1) reconstruction. The ML model is trained on (structure, total energy) relations collected during global minimum energy search runs with an evolutionary algorithm (EA). While being built, the ML model is used to guide the EA, thereby speeding up the overall rate by which the EA succeeds. Inspection of the local atomic potentials emerging from the model further shows chemically intuitive patterns.

OriginalsprogEngelsk
Artikelnummer026102
TidsskriftPhysical Review Letters
Vol/bind120
Nummer2
Antal sider5
ISSN0031-9007
DOI
StatusUdgivet - 12 jan. 2018

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