Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging.

ABSTRACT: Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional-step-restricted second-order truncated expansion-molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In this paper, we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence

SUBMITTER: Raggi G 

PROVIDER: S-EPMC7304864 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature