Machine learning based energy-free structure predictions of molecules, transition states, and solids
Accurate computational prediction of atomistic structure with traditional methods is challenging. The authors report a kernel-based machine learning model capable of reconstructing 3D atomic coordinates from predicted interatomic distances across a variety of system classes.
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-07-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-021-24525-7 |