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arXiv 1903.09010physics.chem-ph
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密度泛函的非共价量子机器学习修正

Non-covalent quantum machine learning corrections to density functionals

Pál D. Mezei, O. Anatole von Lilienfeld

摘要:

我们提出了非共价量子机器学习校正六个物理动机的密度泛函系统误差。我们证明,丢失的大量非局部和非加性物理效应可以通过量子机器学习模型来恢复。这些模型无缝地解释了各种类型的非共价相互作用,并能够准确预测解离曲线。修正改善了分子的两体和三体相互作用的描述,在大型水簇的关键,并提供了一个合理的原子分辨率图片的近似密度泛函的相互作用能的错误,可以是有用的信息,在更准确的密度泛函的发展。我们表明,在足够的训练情况下,校正比标准的分子力学色散校正更灵活,因此它可以适用于许多色散校正的密度泛函失败的情况下,如氢键。

英文摘要:

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.

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