Bereau Tristan, Andrienko Denis, von Lilienfeld O Anatole
Max Planck Institute for Polymer Research , Ackermannweg 10, 55128 Mainz, Germany.
Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, Department of Chemistry, University of Basel , 4056 Basel, Switzerland.
J Chem Theory Comput. 2015 Jul 14;11(7):3225-33. doi: 10.1021/acs.jctc.5b00301.
Accurate representation of the molecular electrostatic potential, which is often expanded in distributed multipole moments, is crucial for an efficient evaluation of intermolecular interactions. Here we introduce a machine learning model for multipole coefficients of atom types H, C, O, N, S, F, and Cl in any molecular conformation. The model is trained on quantum-chemical results for atoms in varying chemical environments drawn from thousands of organic molecules. Multipoles in systems with neutral, cationic, and anionic molecular charge states are treated with individual models. The models' predictive accuracy and applicability are illustrated by evaluating intermolecular interaction energies of nearly 1,000 dimers and the cohesive energy of the benzene crystal.
分子静电势通常用分布多极矩展开，其精确表示对于分子间相互作用的有效评估至关重要。在此，我们引入一种机器学习模型，用于预测任意分子构象中H、C、O、N、S、F和Cl等原子类型的多极系数。该模型基于从数千个有机分子中提取的处于不同化学环境的原子的量子化学结果进行训练。对于中性、阳离子和阴离子分子电荷状态的体系，分别使用不同的模型处理多极矩。通过评估近1000个二聚体的分子间相互作用能以及苯晶体的内聚能，说明了这些模型的预测准确性和适用性。
