Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab-initio accuracy and the computational efficiency of empirical potentials. In this work we propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. As input to the neural network we propose an extendable invariant local molecular descriptor constructed from geometric moments. Their formulation via pairwise distance vectors and tensor contractions allows a very efficient implementation on graphical processing units (GPUs). The atomic species is encoded in the molecular descriptor, which allows the restriction to one neural network for the training of all atomic species in the data set. We demonstrate that the accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models. Due to its high accuracy and efficiency, the proposed machine-learned potentials can be used for any further tasks, for example the optimization of molecular geometries, the calculation of rate constants or molecular dynamics.
翻译:机器学习技术使原子位置和核电荷能够直接映射到潜在的能源表面,其精确度和实验潜力的计算效率几乎为零。在这项工作中,我们提议了一种机器学习方法,用于在进料向神经网络的基础上建造高维潜在能源表面。作为对神经网络的投入,我们建议了一种从几何时刻构造的可扩展的无变本地分子描述器。通过对称远程矢量和电压收缩来配制原子位置和核电荷,可以非常高效地在图形处理器(GPUs)上实施。原子物种被编码在分子描述器中,从而可以限制一个神经网络,用于在数据集中培训所有原子物种。我们证明,在化学和构造空间方面,发达方法的准确性与若干既定机器学习模型的准确性相当。由于它的高度精确性和效率,因此,拟议的机器学习潜力可以用于任何进一步的任务,例如优化分子地理结构、计算率常数或分子动态等。