Self-consistent Validation for Machine Learning Electronic Structure
<|reference_start|>Self-consistent Validation for Machine Learning Electronic Structure: 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.<|reference_end|>
@article{hu2024self-consistent,
    title={Self-consistent Validation for Machine Learning Electronic Structure},
    author={Gengyuan Hu, Gengchen Wei, Zekun Lou, Philip H.S. Torr, Wanli Ouyang,
  Han-sen Zhong, Chen Lin},
    journal={arXiv preprint arXiv:2402.10186},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10186},
    primaryClass={cs.LG physics.chem-ph physics.comp-ph}
}