Source code for constructing local Gaussian process surrogate models for accelerated Bayesian force field optimization. The code is adaptable to any quantity-of-interest in chemistry in physics applications with appropriate updates to training and test data matrices as well as Gaussian process mean and kernel functions.
LGPMD v1.0 - Local Gaussian process surrogate model for radial distribution functions of monatomic fluids. Hyperparameter training performed using leave-one-out log marginal likelihood maximization over a fixed hyperparameter space.
The source code development was supported by the National Science Foundation under award number CBET-1847340. Developed at the University of Utah, Department of Chemical Engineering by Brennon Shanks, Harry Sullivan and Michael P. Hoepfner.