StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks
Accurately predicting the elastic properties of crystalline solids is vital for computational materials science. However, traditional atomistic-scale ab initio approaches are computationally intensive, especially for studying complex materials with a large number of atoms in a unit cell. We introduc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2023-12-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.5.043198 |