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Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations.
- Source :
-
Physical review letters [Phys Rev Lett] 2019 Dec 13; Vol. 123 (24), pp. 245701. - Publication Year :
- 2019
-
Abstract
- The investigation of the microscopic processes underlying structural phase transformations in solids is extremely challenging for both simulation and experiment. Atomistic simulations of solid-solid phase transitions require extensive sampling of the corresponding high-dimensional and often rugged energy landscape. Here, we propose a rigorous construction of a 1D path collective variable that is used in combination with enhanced sampling techniques for efficient exploration of the transformation mechanisms. The path collective variable is defined in a space spanned by global classifiers that are derived from local structural units. A reliable identification of the local structural environments is achieved by employing a neural-network-based classification scheme. The proposed path collective variable is generally applicable and enables the investigation of both transformation mechanisms and kinetics.
Details
- Language :
- English
- ISSN :
- 1079-7114
- Volume :
- 123
- Issue :
- 24
- Database :
- MEDLINE
- Journal :
- Physical review letters
- Publication Type :
- Academic Journal
- Accession number :
- 31922858
- Full Text :
- https://doi.org/10.1103/PhysRevLett.123.245701