Self-consistent Validation for Machine Learning Electronic Structure
Gengyuan Hu Gengchen Wei Zekun Lou Philip H. S. Torr Wanli Ouyang Han-sen Zhong Chen Lin
Machine learning has emerged as a significant approach to efficiently tackle
electronic structure problems. Despite its potential, there is less guarantee
for the model to generalize to unseen data that hinders its application in
real-world scenarios. To address this issue, a technique has been proposed to
estimate the accuracy of the predictions. This method integrates machine
learning with self-consistent field methods to achieve both low validation cost
and interpret-ability. This, in turn, enables exploration of the model's
ability with active learning and instills confidence in its integration into
real-world studies.