Self-consistent Validation for Machine Learning Electronic Structure

• URL: http://arxiv.org/abs/2402.10186v1
• Date: Thu, 15 Feb 2024 18:41:35 GMT
• Title: Self-consistent Validation for Machine Learning Electronic Structure
• Authors: Gengyuan Hu, Gengchen Wei, Zekun Lou, Philip H.S. Torr, Wanli Ouyang, Han-sen Zhong, Chen Lin
• Abstract summary: 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.
• Score: 81.54661501506185
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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