在带隙上训练机器学习密度函数。

IF 5.7 1区 化学 Q2 CHEMISTRY, PHYSICAL
Journal of Chemical Theory and Computation Pub Date : 2024-09-10 Epub Date: 2024-08-23 DOI:10.1021/acs.jctc.4c00999
Kyle Bystrom, Stefano Falletta, Boris Kozinsky
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引用次数: 0

摘要

系统性地低估带隙是半局部密度泛函理论(DFT)最基本的挑战之一。带隙问题不仅阻碍了 DFT 在预测电子特性方面的应用,还与自相互作用和脱ocalization 误差密切相关,这使得用 DFT 研究电荷转移机制变得十分困难。为了扩大解决带隙问题的可用工具范围,我们设计了一种基于高斯过程的机器学习密度函数方法,以明确拟合单粒子能级。我们还引入了密度矩阵的非局部特征,其表现力足以拟合这些单粒子能级。结合这些发展,我们训练出了精确交换能的机器学习函数,它能预测多种分子的分子能隙和反应能,与参考的混合 DFT 计算结果非常一致。此外,我们的模型在仅根据分子数据进行训练的同时,还能预测固体中极子的合理形成能,展示了其可移植性和稳健性。我们讨论了如何将这种方法推广到全交换相关函数,从而为设计最先进的分子和材料电子特性预测函数铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Training Machine-Learned Density Functionals on Band Gaps.

Training Machine-Learned Density Functionals on Band Gaps.

The systematic underestimation of band gaps is one of the most fundamental challenges in semilocal density functional theory (DFT). In addition to hindering the application of DFT to predicting electronic properties, the band gap problem is intimately related to self-interaction and delocalization errors, which make the study of charge transfer mechanisms with DFT difficult. To expand the range of available tools for addressing the band gap problem, we design an approach for machine learning density functionals based on Gaussian processes to explicitly fit single-particle energy levels. We also introduce nonlocal features of the density matrix that are expressive enough to fit these single-particle levels. Combining these developments, we train a machine-learned functional for the exact exchange energy that predicts molecular energy gaps and reaction energies of a wide range of molecules in excellent agreement with reference hybrid DFT calculations. In addition, while being trained solely on molecular data, our model predicts reasonable formation energies of polarons in solids, showcasing its transferability and robustness. We discuss how this approach can be generalized to full exchange-correlation functionals, thus paving the way to the design of state-of-the-art functionals for the prediction of electronic properties of molecules and materials.

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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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