Margraf Johannes T, Reuter Karsten
Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Lichtenbergstraße 4, D-85747, Garching, Germany.
Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195, Berlin, Germany.
Nat Commun. 2021 Jan 12;12(1):344. doi: 10.1038/s41467-020-20471-y.
Density-functional theory (DFT) is a rigorous and (in principle) exact framework for the description of the ground state properties of atoms, molecules and solids based on their electron density. While computationally efficient density-functional approximations (DFAs) have become essential tools in computational chemistry, their (semi-)local treatment of electron correlation has a number of well-known pathologies, e.g. related to electron self-interaction. Here, we present a type of machine-learning (ML) based DFA (termed Kernel Density Functional Approximation, KDFA) that is pure, non-local and transferable, and can be efficiently trained with fully quantitative reference methods. The functionals retain the mean-field computational cost of common DFAs and are shown to be applicable to non-covalent, ionic and covalent interactions, as well as across different system sizes. We demonstrate their remarkable possibilities by computing the free energy surface for the protonated water dimer at hitherto unfeasible gold-standard coupled cluster quality on a single commodity workstation.
密度泛函理论（DFT）是一种基于电子密度来描述原子、分子和固体基态性质的严格且（原则上）精确的框架。虽然计算效率高的密度泛函近似（DFA）已成为计算化学中的重要工具，但其对电子关联的（半）局域处理存在许多众所周知的问题，例如与电子自相互作用相关的问题。在此，我们提出一种基于机器学习（ML）的DFA（称为核密度泛函近似，KDFA），它是纯粹的、非局域的且可转移的，并且可以使用完全定量的参考方法进行有效训练。这些泛函保留了常见DFA的平均场计算成本，并被证明适用于非共价、离子和共价相互作用，以及不同的系统规模。我们通过在单个商用工作站上以迄今不可行的金标准耦合簇质量计算质子化水二聚体的自由能表面，展示了它们显著的可能性。
