AI 中文总结
本文提出一种机器学习模型，基于内坐标学习Hessian矩阵，可高效预测肽链与小型蛋白质的Hessian矩阵，用于计算热化学性质，解决量子力学计算成本高的问题。
AI 中文摘要
分子Hessian矩阵在描述分子振动、轨迹和优化路径方面具有关键作用。通过标准量子力学方法显式计算该矩阵，对于中大型体系而言计算成本极高，且在许多应用场景中甚至并非必需。机器学习方法可作为高效解决计算难题的捷径。本文将提出一种机器学习模型，能够预测由数千个原子构成的生物体系的Hessian矩阵。该方法基于学习内坐标下的Hessian矩阵，具有分子旋转与平移不变性，且随体系规模的扩展表现出良好的可扩展性。训练在简单氨基酸数据集上完成，因为氨基酸是构成更大蛋白质的基本单元。通过预测得到的Hessian矩阵，可在简谐近似框架下计算热化学性质，包括焓、熵、吉布斯自由能以及零点振动能。
英文摘要
Molecular Hessians have a key role in describing molecular vibrations, trajectories and optimization paths. An explicit calculation of them through standard quantum mechanical methods can be computationally expensive for medium-large systems, and in many applications not even needed. Machine learning methods can be a shortcut to tackle efficiently the computational difficulties. This paper will present a ML model able to predict the Hessian matrix of biological system made of thousands of atoms. The method, based on learning the Hessian in internal coordinates is intrinsically invariant to molecular rotations and translations, and has a very good scaling with the systems' size. The training was performed on a dataset of simple aminoacids, as they constitute the building blocks of larger proteins. From the predicted Hessian matrix it is possible to calculate thermochemical properties within the harmonic approximations, among them enthalpies, entropies, Gibbs' free energies, and zero point vibrational energies.
