Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning
Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning
The training set of atomic configurations is key to the performance of any Machine Learning Force Field (MLFF) and, as such, the training set selection determines the applicability of the MLFF model for predictive molecular simulations. However, most atomistic reference datasets are inhomogeneously distributed across configurational space (CS), thus choosing …