Noncovalent interactions are of fundamental importance across the disciplines of chemistry, materials science, and biology. Quantum chemical calculations on noncovalently bound complexes, which allow for the quantification of properties such as binding energies and geometries, play an essential role in advancing our understanding of, and building models for, a vast array of complex processes involving molecular association or self-assembly. Because of its relatively modest computational cost, second-order Møller-Plesset perturbation (MP2) theory is one of the most widely used methods in quantum chemistry for studying noncovalent interactions. MP2 is, however, plagued by serious errors due to its incomplete treatment of electron correlation, especially when modeling van der Waals interactions and π-stacked complexes. Here we present spin-network-scaled MP2 (SNS-MP2), a new semi-empirical MP2-based method for dimer interaction-energy calculations. To correct for errors in MP2, SNS-MP2 uses quantum chemical features of the complex under study in conjunction with a neural network to reweight terms appearing in the total MP2 interaction energy. The method has been trained on a new data set consisting of over 200 000 complete basis set (CBS)-extrapolated coupled-cluster interaction energies, which are considered the gold standard for chemical accuracy. SNS-MP2 predicts gold-standard binding energies of unseen test compounds with a mean absolute error of 0.04 kcal mol⁻¹ (root-mean-square error 0.09 kcal mol⁻¹), a 6- to 7-fold improvement over MP2. To the best of our knowledge, its accuracy exceeds that of all extant density functional theory- and wavefunction-based methods of similar computational cost, and is very close to the intrinsic accuracy of our benchmark coupled-cluster methodology itself. Furthermore, SNS-MP2 provides reliable per-conformation confidence intervals on the predicted interaction energies, a feature not available from any alternative method.

中文翻译：

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利用神经网络提高Møller-Plesset微扰理论的准确性

非共价相互作用在化学，材料科学和生物学学科中至关重要。对非共价键结合的复合物进行量子化学计算，可以量化诸如结合能和几何形状之类的特性，在增进我们对涉及分子缔合或自组装的众多复杂过程的理解和建立模型中起着至关重要的作用。由于其相对适度的计算成本，二阶Møller-Plesset微扰（MP2）理论是研究非共价相互作用的量子化学中使用最广泛的方法之一。但是，由于MP2对电子相关性的处理不完全，MP2受到严重错误的困扰，尤其是在对范德华相互作用和π堆积复合物进行建模时。我们在这里介绍自旋网络缩放的MP 2（SNS-MP2），一种基于半经验MP2的新的二聚体相互作用能计算方法。为了校正MP2中的错误，SNS-MP2将研究中的复合物的量子化学特征与神经网络结合使用，以对总MP2相互作用能中出现的项进行加权。该方法已在新数据集上进行了训练，该新数据集由超过20万个完整基集（CBS）外推的耦合簇相互作用能组成，这被认为是化学准确性的金标准。SNS-MP2预测看不见的测试化合物的金标准结合能，平均绝对误差为0.04 kcal mol ^-1（均方根误差为0.09 kcal mol ^-1），比MP2提升了6到7倍。据我们所知，其准确性超过了所有现有的基于密度泛函理论和基于波函数的类似计算成本的方法，并且非常接近于我们的基准耦合聚类方法本身的固有准确性。此外，SNS-MP2在预测的交互作用能量上提供了可靠的每个构象置信区间，这是任何其他方法都无法提供的功能。