Noncovalent Quantum Machine Learning Corrections to Density Functionals,Journal of Chemical Theory and Computation

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Noncovalent Quantum Machine Learning Corrections to Density Functionals
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2020-03-04 , DOI: 10.1021/acs.jctc.0c00181
Pál D. Mezei ₁ , O. Anatole von Lilienfeld ₁

Affiliation

We present noncovalent quantum machine learning corrections to six physically motivated density functionals with systematic errors. We demonstrate that the missing massively nonlocal and nonadditive physical effects can be recovered by quantum machine learning models. The models seamlessly account for various types of noncovalent interactions and enable accurate predictions of dissociation curves. The correction improves the description of molecular two- and three-body interactions crucial in large water clusters and provides a reasonable atomic-resolution picture about the interaction energy errors of approximate density functionals that can be useful information in the development of more accurate density functionals. We show that given sufficient training instances the correction is more flexible than standard molecular mechanical dispersion corrections, and thus it can be applied for cases where many dispersion corrected density functionals fail, such as hydrogen bonding.

中文翻译：

非共价量子机器学习对密度泛函的校正

我们提出了对具有系统误差的六个物理密度函数的非共价量子机器学习校正。我们证明了量子机器学习模型可以弥补缺失的大量非局部和非累加的物理效应。这些模型无缝地考虑了各种类型的非共价相互作用，并能够准确预测解离曲线。该校正改进了对大型水团簇中至关重要的分子两体和三体相互作用的描述，并提供了有关近似密度泛函的相互作用能误差的合理原子分辨率图像，这对于开发更精确的密度泛函可能是有用的信息。

更新日期：2020-04-24

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