Nature Communications (Jul 2021)

Machine learning based energy-free structure predictions of molecules, transition states, and solids

  • Dominik Lemm,
  • Guido Falk von Rudorff,
  • O. Anatole von Lilienfeld

DOI
https://doi.org/10.1038/s41467-021-24525-7
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 10

Abstract

Read online

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.