D. E. Shaw Research, New York, New York 10036, USA.
J Chem Phys. 2017 Oct 28;147(16):161725. doi: 10.1063/1.4986081.
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 (root-mean-square error 0.09 kcal mol), 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.
非共价相互作用在化学、材料科学和生物学等多个领域都具有重要意义。量子化学计算可以对非共价结合复合物进行量化，从而确定结合能和几何形状等性质，对于深入了解和构建涉及分子缔合或自组装的各种复杂过程的模型起着至关重要的作用。由于其计算成本相对较低，二阶 Møller-Plesset 微扰（MP2）理论是量子化学中研究非共价相互作用最广泛使用的方法之一。然而，由于其对电子相关的不完全处理，MP2 存在严重的误差，尤其是在建模范德华相互作用和π堆积复合物时。在这里，我们提出了自旋网络缩放 MP2（SNS-MP2），这是一种新的半经验 MP2 基方法，用于二聚体相互作用能的计算。为了纠正 MP2 的误差，SNS-MP2 使用所研究复合物的量子化学特征，并结合神经网络对总 MP2 相互作用能中的项进行重新加权。该方法已经在一个由超过 200000 个完全基组（CBS）外推的耦合簇相互作用能组成的新数据集上进行了训练，这些数据被认为是化学精度的金标准。SNS-MP2 预测未见测试化合物的金标准结合能的平均绝对误差为 0.04 kcal mol（均方根误差 0.09 kcal mol），比 MP2 提高了 6 到 7 倍。据我们所知，它的准确性超过了所有具有类似计算成本的现有密度泛函理论和波函数方法，并且非常接近我们基准耦合簇方法本身的固有准确性。此外，SNS-MP2 还提供了预测相互作用能的可靠每构象置信区间，这是任何其他方法都无法提供的功能。
