基于新一代密度泛函和神经网络势能面的量子反应动力学计算

Quantum Reaction Dynamics Based on a New Generation Density Functional and Neural Network Potential Energy Surfaces

介绍了近年来发展起来的新一代密度泛函XYG3及利用神经网络构造分子体系势能面的最新进展。以H3和CH5等体系为实例,表明基于高效准确的密度泛函电子结构计算,与神经网络高精度势能面构造的理想结合,可以得到可靠的化学动力学结果,并有望用于更大更复杂的体系。

Recent progresses on a new generation density functional XYG3 and the construction of potential energy surfaces using neural networks are reviewed in this article. Using H3 and CH, systems as illustrative examples, it is concluded that highly reliable dynamics results can be obtained from the combination of electronic structure calculations based on efficient and accurate density functionals and accurate potential energy surfaces using neural networks. It holds promise for future applications in larger and more complicated systems.