Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States.
Materials Science and Engineering, University of California San Diego, La Jolla, California 92093, United States.
J Chem Theory Comput. 2022 Jun 14;18(6):3410-3426. doi: 10.1021/acs.jctc.2c00050. Epub 2022 May 4.
We investigate the interplay between functional-driven and density-driven errors in different density functional approximations within density functional theory (DFT) and the implications of these errors for simulations of water with DFT-based data-driven potentials. Specifically, we quantify density-driven errors in two widely used dispersion-corrected functionals derived within the generalized gradient approximation (GGA), namely BLYP-D3 and revPBE-D3, and two modern -GGA functionals, namely strongly constrained and appropriately normed (SCAN) and B97M-rV. The effects of functional-driven and density-driven errors on the interaction energies are first assessed for the water clusters of the BEGDB dataset. Further insights into the nature of functional-driven errors are gained from applying the absolutely localized molecular orbital energy decomposition analysis (ALMO-EDA) to the interaction energies, which demonstrates that functional-driven errors are strongly correlated with the nature of the interactions. We discuss cases where density-corrected DFT (DC-DFT) models display higher accuracy than the original DFT models and cases where reducing the density-driven errors leads to larger deviations from the reference energies due to the presence of large functional-driven errors. Finally, molecular dynamics simulations are performed with data-driven many-body potentials derived from DFT and DC-DFT data to determine the effect that minimizing density-driven errors has on the description of liquid water. Besides rationalizing the performance of widely used DFT models of water, we believe that our findings unveil fundamental relations between the shortcomings of some common DFT approximations and the requirements for accurate descriptions of molecular interactions, which will aid the development of a consistent, DFT-based framework for the development of data-driven and machine-learned potentials for simulations of condensed-phase systems.
我们研究了不同密度泛函理论（DFT）中功能驱动和密度驱动误差之间的相互作用，以及这些误差对基于 DFT 的数据驱动势模拟水的影响。具体来说，我们量化了广义梯度近似（GGA）内两个广泛使用的色散校正泛函（BLYP-D3 和 revPBE-D3）和两个现代 GGA 泛函（SCAN 和 B97M-rV）中的密度驱动误差。首先，我们评估了功能驱动和密度驱动误差对 BEGDB 数据集水团簇相互作用能的影响。通过对相互作用能应用绝对局域分子轨道能量分解分析（ALMO-EDA），我们进一步深入了解了功能驱动误差的性质，证明了功能驱动误差与相互作用的性质密切相关。我们讨论了密度校正 DFT（DC-DFT）模型比原始 DFT 模型具有更高精度的情况，以及降低密度驱动误差会由于存在较大的功能驱动误差而导致与参考能量偏差较大的情况。最后，通过使用从 DFT 和 DC-DFT 数据导出的数据驱动多体势进行分子动力学模拟，确定了最小化密度驱动误差对液体水描述的影响。除了合理说明水的广泛使用的 DFT 模型的性能外，我们相信我们的发现揭示了一些常见 DFT 近似的缺点与分子相互作用的准确描述之间的基本关系，这将有助于开发一致的、基于 DFT 的框架，用于开发数据驱动和机器学习势的模拟凝聚相系统。
