物理化学学报 >> 2019, Vol. 35 >> Issue (2): 145-157.doi: 10.3866/PKU.WHXB201803281

所属专题: 2017年中国科学院新增院士特刊

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多原子反应体系的高精度拟合势能面

傅碧娜*(),陈俊,刘天辉,邵科杰,张东辉*()   

  • 收稿日期:2018-03-01 发布日期:2018-07-10
  • 通讯作者: 傅碧娜,张东辉 E-mail:bina@dicp.ac.cn;zhangdh@dicp.ac.cn
  • 作者简介:傅碧娜,1981年生。2004年本科毕业于大连理工大学物理系,2009年博士毕业于中国科学院大连化学物理研究所。现为中科院大连化学物理研究所分子反应动力学国家重点实验室研究员。主要研究方向为气相多原子分子反应以及气相-表面化学反应的势能面构建和动力学研究|张东辉,1967年生。1989年本科毕业于复旦大学物理系,1994年博士毕业于纽约大学。2017年当选中国科学院院士。现为中国科学院大连化学物理研究所分子反应动力学国家重点实验室主任。主要研究方向为气相和表面化学反应动力学理论和计算研究
  • 基金资助:
    国家自然科学基金(21722307);国家自然科学基金(21673233);国家自然科学基金(21590804);国家自然科学基金(21433009);国家自然科学基金(21688102);中国科学院战略性先导科技专项(B类)(XDB17000000)

Highly Accurately Fitted Potential Energy Surfaces for Polyatomic Reactive Systems

Bina FU*(),Jun CHEN,Tianhui LIU,Kejie SHAO,Dong H. ZHANG*()   

  • Received:2018-03-01 Published:2018-07-10
  • Contact: Bina FU,Dong H. ZHANG E-mail:bina@dicp.ac.cn;zhangdh@dicp.ac.cn
  • Supported by:
    the National Natural Science Foundation of China(21722307);the National Natural Science Foundation of China(21673233);the National Natural Science Foundation of China(21590804);the National Natural Science Foundation of China(21433009);the National Natural Science Foundation of China(21688102);the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB17000000)

摘要:

本文介绍了近几年来我们组构建多原子反应体系的高精度拟合势能面的进展。我们基于神经网络(NN)方法,成功构建了多原子气相分子体系和气相分子在金属表面解离的一系列势能面。这些势能面的拟合精度相当高,并且经过了严格的量子动力学测试,能广泛应用到动力学研究中。我们还提出了一种新的置换不变势能面的拟合方法,即基本不变量神经网络方法(FI-NN)。基本不变量的使用极大地减少了神经网络输入层多项式的个数,有效提高了势能面的计算速度。

关键词: 势能面, 神经网络, 反应动力学, 基本不变量, 从头算

Abstract:

Over the past decade, significant progress has been made in theoretical and experimental research in the field of chemical reaction dynamics, moving from triatomic reactions to larger polyatomic reactions. This has challenged the theoretical and computational approaches to polyatomic reaction dynamics in two major areas: the potential energy surface and the dynamics. Highly accurate potential energy surfaces are essential for achieving accurate dynamical information in quantum dynamics calculations. The increased number of degrees of freedom in larger systems poses a significant challenge to the accurate construction of potential energy surfaces. Recently, there has been substantial progress in the development of potential energy surfaces for polyatomic reactive systems. In this article, we review the recent developments made by our group in constructing highly accurately fitted potential energy surfaces for polyatomic reactive systems, based on a neural network approach. A key advantage of the neural network approach is its more faithful representation of the ab initio points. We recently proposed a systematic procedure, based on neural network fitting, for the construction of accurate potential energy surfaces with very small root mean square errors. Based on the neural network approach, we successfully developed potential energy surfaces for polyatomic reactions in the gas phase, including the reactive systems OH3, HOCO, and CH5, and the dissociation of gas-phase molecules on metal surfaces, such as H2O on the Cu(111) surface. These potential energy surfaces were fitted to an unprecedented level of accuracy, representing the most accurate potential energy surfaces calculated for these systems, and were rigorously tested using quantum dynamics calculations. The quantum dynamics calculations based on these potential energy surfaces produce accurate results, which are in good agreement with experiments. We have also proposed a new method for developing permutationally invariant potential energy surfaces, named fundamental-invariant neural networks. Mathematically, fundamental invariants are used to finitely generate the permutation-invariant polynomial ring; thus, fundamental-invariant neural networks can approximate any function to arbitrary accuracy. The use of fundamental invariants minimizes the size of the input permutation-invariant polynomials, which reduces the evaluation time for potential energy calculations, especially for polyatomic systems. Potential energy surfaces for OH3 and CH4 were constructed using fundamental-invariant neural networks, with their accuracies confirmed by full-dimensional quantum dynamics and bound-state calculations. These developments in the construction of highly accurate potential energy surfaces are expected to extend the theoretical study of reaction dynamics to larger and more complex systems.

Key words: Potential energy surface, Neural networks, Reaction dynamics, Fundamental invariants, Ab initio

MSC2000: 

  • O643