Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Hu, Gengyuan, Wei, Gengchen, Lou, Zekun, Torr, Philip H. S., Ouyang, Wanli, Zhong, Han-sen, Lin, Chen
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4391900170