期刊
PHYSICAL REVIEW LETTERS
卷 123, 期 24, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.123.245701
关键词
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资金
- Alexander von Humboldt Foundation
- National Science Foundation through the Materials Research Science and Engineering Center (MRSEC) program [DMR-1420073]
- National Science Foundation [CHE-1565980]
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 arc 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.
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