John A. Paulson School of Engineering and Applied Sciences, Harvard University, , Cambridge, Massachusetts 02138, United States.
J Chem Theory Comput. 2022 Apr 12;18(4):2180-2192. doi: 10.1021/acs.jctc.1c00904. Epub 2022 Mar 2.
Machine learning (ML) has recently gained attention as a means to develop more accurate exchange-correlation (XC) functionals for density functional theory, but functionals developed thus far need to be improved on several metrics, including accuracy, numerical stability, and transferability across chemical space. In this work, we introduce a set of nonlocal features of the density called the CIDER formalism, which we use to train a Gaussian process model for the exchange energy that obeys the critical uniform scaling rule for exchange. The resulting CIDER exchange functional is significantly more accurate than any semilocal functional tested here, and it has good transferability across main-group molecules. This work therefore serves as an initial step toward more accurate exchange functionals, and it also introduces useful techniques for developing robust, physics-informed XC models via ML.
机器学习 (ML) 最近作为一种方法引起了关注，旨在为密度泛函理论开发更准确的交换相关 (XC) 泛函，但迄今为止开发的泛函在准确性、数值稳定性和化学空间的可转移性等几个方面需要改进。在这项工作中，我们引入了一组称为 CIDER 形式的密度的非局域特征，我们使用它来训练一个符合交换临界均匀标度规则的高斯过程模型，以用于交换能。由此产生的 CIDER 交换泛函比这里测试的任何半局域泛函都要准确得多，并且在主族分子之间具有良好的可转移性。因此，这项工作是朝着更准确的交换泛函迈出的第一步，并且它还通过 ML 为开发稳健、物理信息丰富的 XC 模型引入了有用的技术。
