Training Machine-Learned Density Functionals on Band Gaps,Journal of Chemical Theory and Computation

当前位置： X-MOL 学术 › J. Chem. Theory Comput. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Training Machine-Learned Density Functionals on Band Gaps
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2024-08-23 , DOI: 10.1021/acs.jctc.4c00999
Kyle Bystrom ₁ , Stefano Falletta ₁ , Boris Kozinsky _{1,

2}

Affiliation

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.

中文翻译：

训练带隙上的机器学习密度泛函

带隙的系统性低估是半局域密度泛函理论（DFT）中最基本的挑战之一。带隙问题除了阻碍DFT在预测电子性质方面的应用外，还与自相互作用和离域误差密切相关，这使得利用DFT研究电荷转移机制变得困难。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泛函方法，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，这些特征的表现力足以适应这些单粒子水平。结合这些进展，我们训练了一个用于精确交换能量的机器学习函数，该函数可以预测各种分子的分子能隙和反应能量，与参考混合 DFT 计算非常一致。此外，在仅接受分子数据训练的同时，我们的模型预测了固体中极化子的合理形成能，展示了其可转移性和鲁棒性。我们讨论如何将这种方法推广到完整的交换相关泛函，从而为预测分子和材料的电子特性的最先进的泛函设计铺平道路。

更新日期：2024-08-23

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南