Machine-learning potentials for crystal defects
Abstract Decades of advancements in strategies for the calculation of atomic interactions have culminated in a class of methods known as machine-learning interatomic potentials (MLIAPs). MLIAPs dramatically widen the spectrum of materials systems that can be simulated with high physical fidelity, in...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer International Publishing,
2022-08-19T12:58:50Z.
|
| Subjects: | |
| Online Access: | Get fulltext |