Machine learning based energy-free structure predictions of molecules, transition states, and solids - Dominik Lemm (Nature Portfolio, 2021)
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.