Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 149, Issue 12, Pages -Publisher
AIP Publishing
DOI: 10.1063/1.5040005
Keywords
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Funding
- Novartis Universitat Basel Excellence Scholarship for Life Sciences
- Swiss National Science Foundation [P300P2-158407, P300P2-174475]
- National Science Foundation [OCI-1053575]
- NSF [ACI-1445606]
- DOE [DE-AC02-05CH11231]
- Swiss National Supercomputing Center in Lugano [s700]
- Swiss National Science Foundation (SNF) [P300P2_158407, P300P2_174475] Funding Source: Swiss National Science Foundation (SNF)
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Current machine-learning methods to reproduce ab initio potential energy landscapes suffer from an unfavorable computational scaling with respect to the number of chemical species. In this work, we propose a new approach by using optimized symmetry functions to explore similarities of structures in multicomponent systems in order to yield linear complexity. We combine these symmetry functions with the charge equilibration via neural network technique, a reliable artificial neural network potential for ionic materials, and apply this method to study alkali-halide materials MX with 6 chemical species (M = {Li, Na, K} and X = {F, Cl, Br}). Our results show that our approach provides good agreement both with experimental and DFT reference data of many physical and structural properties for any chemical combination. Published by AIP Publishing.
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