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...
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| Format: | Article |
| Language: | English |
| Published: |
Springer International Publishing
2022
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| Online Access: | https://hdl.handle.net/1721.1/144364 |