In this work, a machine learning mapping approach for predicting the properties of atomistic systems is reported. Within this approach, the atomic orbital overlap, density, or Kohn-Sham (KS) Fock matrix elements obtained at a low level of theory such as extended tight-binding have been used as input features to predict the electric field gradient (EFG) tensors at a higher level of theory such as those obtained with hybrid functionals. It is shown that the machine-learning-predicted EFG tensors can be used to compute spin relaxation rates of several ions in aqueous solutions. From only a fraction of data used in direct calculation, one can predict the quadrupolar isotropic spin relaxation rates with good accuracy, achieving relative errors between about 2–8% for different ions.

中文翻译：

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用于计算自旋松弛动力学的机器学习映射方法


在这项工作中，报道了一种用于预测原子系统特性的机器学习映射方法。在这种方法中，在低理论水平（如扩展紧密结合）中获得的原子轨道重叠、密度或 Kohn-Sham （KS） Fock 矩阵元素已被用作输入特征，以预测更高理论水平的电场梯度 （EFG） 张量，例如通过混合泛函获得的张量。结果表明，机器学习预测的 EFG 张量可用于计算水溶液中多个离子的自旋弛豫率。仅从直接计算中使用的一小部分数据中，就可以很好地预测四极各向同性自旋弛豫速率，不同离子的相对误差约为 2-8%。