多原子反应体系的高精度拟合势能面

doi:10.3866/PKU.WHXB201803281

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

所属专题：中国科学院新增院士专刊

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*()

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，理论与计算化学研究中心，辽宁大连 116023

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*()

State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical and Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning Province, P. R. China

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/Abstract

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

傅碧娜, 陈俊, 刘天辉, 邵科杰, 张东辉. 多原子反应体系的高精度拟合势能面[J]. 物理化学学报, 2019, 35(2), 145-157. doi: 10.3866/PKU.WHXB201803281

Bina FU, Jun CHEN, Tianhui LIU, Kejie SHAO, Dong H. ZHANG. Highly Accurately Fitted Potential Energy Surfaces for Polyatomic Reactive Systems[J]. Acta Phys. -Chim. Sin. 2019, 35(2), 145-157. doi: 10.3866/PKU.WHXB201803281

链接本文: https://www.whxb.pku.edu.cn/CN/10.3866/PKU.WHXB201803281

https://www.whxb.pku.edu.cn/CN/Y2019/V35/I2/145

参考文献

1	Bowman J. M. ; Schatz G. C. Annu. Rev. Phys. Chem. 1995, 46, 169.
2	Zhang J. Z. H. ; Dai J. ; Zhu W. J. Phys. Chem. A 1997, 101, 2746.
3	Miller M. H. J. Chem. Soc. Faraday Trans. 1997, 93, 685.
4	Clary D. C. Science 1998, 279, 1879.
5	Althorpe S. C. ; Clary D. C. Annu. Rev. Phys. Chem. 2003, 54, 493.
6	Balint-Kurti G. G. Int. Rev. Phys. Chem. 2008, 27, 507.
7	Guo H. Int. Rev. Phys. Chem. 2012, 31, 1.
8	Yarkony D. R. Chem. Rev. 2012, 112, 481.
9	Nyman G. ; Yu H.-G. Int. Rev. Phys. Chem. 2013, 32, 39.
10	Otto R. ; Ma J. ; Ray A. W. ; Daluz J. S. ; Li J. ; Guo H. ; Continetti R. E. Science 2014, 343, 396.
11	Liu K. Annu. Rev. Phys. Chem. 2001, 52, 139.
12	Fernandez-Alonso F. ; Zare R. N. Annu. Rev. Phys. Chem. 2002, 53, 67.
13	Balucani N. ; Capozza G. ; Leonori F. ; Segoloni E. ; Casavecchia P. Int. Rev. Phys. Chem. 2006, 25, 109.
14	Yang X. Annu. Rev. Phys. Chem. 2007, 58, 433.
15	Crim F. F. Proc. Natl. Acad. Sci. USA 2008, 105, 12654.
16	Liu K. Adv. Chem. Phys. 2012, 149, 1.
17	Guo H. ; Liu K. Chem. Sci. 2016, 7, 3992.
18	Qiu M. ; Ren Z. ; Che L. ; Dai D. ; Harich S. A. ; Wang X. ; Yang X. ; Xu C. ; Xie D. ; Gustafsson M. ; et al Science 2006, 311, 1440.
19	Che L. ; Ren Z. ; Wang X. ; Dong W. ; Dai D. ; Wang X. ; Zhang D. H. ; Yang X. ; Sheng L. ; Li G. ; et al Science 2007, 317, 1061.
20	Wang X. ; Dong W. ; Xiao C. ; Che L. ; Ren Z. ; Dai D. ; Wang X. ; Casavecchia P. ; Yang X. ; Jiang B. ; et al Science 2008, 322, 573.
21	Wang T. ; Chen J. ; Yang T. ; Xiao C. ; Sun Z. ; Huang L. ; Dai D. ; Yang X. ; Zhang D. H. Science 2013, 342, 1499.
22	Yang T. ; Chen J. ; Huang L. ; Wang T. ; Xiao C. ; Sun Z. ; Dai D. ; Yang X. ; Zhang D. H. Science 2015, 347, 60.
23	Xiao C. ; Xu X. ; Liu S. ; Wang T. ; Dong W. ; Yang T. ; Sun Z. ; Dai D. ; Xu X. ; Zhang D. H. ; et al Science 2011, 333, 440.
24	Zhang W. ; Zhou Y. ; Wu G. ; Lu Y. ; Pan H. ; Fu B. ; Shuai Q. ; Liu L. ; Liu S. ; Zhang L. ; et al Proc. Natl. Acad. Sci. USA 2010, 107, 12782.
25	Zhang D. H. ; Guo H. Annu. Rev. Phys. Chem. 2016, 67, 135.
26	Zhang Z. ; Zhou Y. ; Zhang D. H. ; Czakó G. ; Bowman J. M. J. Phys. Chem. Lett. 2012, 3, 3416.
27	Liu R. ; Yang M. ; Czakó G. ; Bowman J. M. ; Li J. ; Guo H. J. Phys. Chem. Lett. 2012, 3, 3776.
28	Welsch R. ; Manthe U. J. Phys. Chem. Lett. 2015, 6, 338.
29	Zhao Z. ; Zhang Z. ; Liu S. ; Zhang D. H. Nat. Commun. 2017, 8, 14506.
30	Fu B. ; Shan X. ; Zhang D. H. ; Clary D. C. Chem. Soc. Rev. 2017, 46, 7625.
31	Dawes R. ; Thompson D. L. ; Wagner A. F. ; Minkoff M. J. Chem. Phys. 2008, 128, 084107.
32	Collins M. A. Theor. Chem. Acc. 2002, 108, 313.
33	Braams B. J. ; Bowman J. M. Int. Rev. Phys. Chem. 2009, 13, 577.
34	Bowman J. M. ; Czakó G. ; Fu B. Phys. Chem. Chem. Phys. 2011, 28, 8094.
35	Raff L. M. ; Malshe M. ; Hagan M. ; Doughan D. I. ; Rockley M. G. ; Komanduri R. J. Chem. Phys. 2005, 122, 084104.
36	Chen J. ; Xu X. ; Xu X. ; Zhang D. H. J. Chem. Phys. 2013, 138, 154301.
37	Chen J. ; Xu X. ; Xu X. ; Zhang D. H. J. Chem. Phys. 2013, 138, 221104.
38	Jiang B. ; Guo H. J. Chem. Phys. 2013, 139, 054112.
39	Li J. ; Jiang B. ; Guo H. J. Chem. Phys. 2013, 139, 204103.
40	Shao K. ; Chen J. ; Zhao Z. ; Zhang D. H. J. Chem. Phys. 2016, 145, 071101.
41	Bartlett R. J. ; Musia? M. Rev. Mod. Phys. 2007, 79, 291.
42	Werner H. -J. Adv. Chem. Phys. 1987, 69, 1.
43	Schatz G. C. Rev. Mod. Phys. 1989, 61, 669.
44	Hollebeek T. ; Ho T. S. ; Rabitz H. Annu. Rev. Phys. Chem. 1999, 50, 537.
45	Zhou Y. ; Fu B. ; Wang C. ; Collins M. A. ; Zhang D. H. J. Chem. Phys. 2011, 134, 064323.
46	Fu B. ; Han Y. ; Bowman J. M. ; Angelucci L. ; Balucani N. ; Leonori F. ; Casavecchia P. Proc. Natl. Acad. Sci. USA 2012, 109, 9733.
47	Yang J. ; Shao K. ; Zhang D. ; Shuai Q. ; Fu B. ; Zhang D. H. ; Yang X. J. Phys. Chem. Lett. 2014, 5, 3106.
48	Raff L. M. ; Komanduri R. ; Hagan M. ; Bukkapatnam S. Neural Networks in Chemical Reaction Dynamics; Oxford University Press: New York, NY, USA 2012.
49	Blank T. B. ; Brown S. D. ; Calhoun A. W. ; Doren D. J. J. Chem. Phys. 1995, 103, 4129.
50	Brown D. F. R. ; Gibbs M. N. ; Clary D. C. J. Chem. Phys. 1996, 105, 7597.
51	Manzhos S. ; Wang X. G. ; Dawes R. ; Carrington T. J. Phys. Chem. A 2006, 110, 5295.
52	Behler J. ; Parrinello M. Phys. Rev. Lett. 2007, 98, 146401.
53	Pukrittayakamee A. ; Malshe M. ; Hagan M. ; Raff L. M. ; Narulkar R. ; Bukkapatnam S. ; Komanduri R. J. Chem. Phys. 2009, 130, 134101.
54	Handley C. M. ; Popelier P. L. A. J. Phys. Chem. A 2010, 114, 3371.
55	Behler J. Phys. Chem. Chem. Phys. 2011, 13, 17930.
56	Xu X. ; Chen J. ; Zhang D. H. Chin. J. Chem. Phys. 2014, 27, 373.
57	Hagan M. ; Menhaj M. IEEE Trans. Neural Networks 1994, 5, 989.
58	Clary, D. C. Rate Coefficients in Astrochemistry; Millar, T. J., Williams, D. A., Eds. ; Astrophysics and Space Science Library; Springer: The Netherlands, 1988; Vol. 146; Chapter 1, pp. 1–16.
59	Warnatz, J. Combustion Chemistry; Gardiner, W., Ed. ; Springer-Verlag: New York, NY, USA, 1985; Chapter 5.
60	Schatz G. C. ; Elgersma H. Chem. Phys. Lett. 1980, 73, 21.
61	Ochoa de Aspuru G. ; Clary D. C. J. Phys. Chem. A 1998, 102, 9631.
62	Pogrebnya S. K. ; Palma J. ; Clary D. C. ; Echave J. Phys. Chem. Chem. Phys. 2000, 2, 693.
63	Wu G.-S. ; Schatz G. C. ; Lendvay G. ; Fang D. -C. ; Harding L. B. J. Chem. Phys. 2000, 113, 3150.
64	Yang M. H. ; Zhang D. H. ; Collins M. A. ; Lee S. Y. J. Chem. Phys. 2001, 115, 174.
65	Finlayson-Pitts, B. J. ; Pitts, J. N. Chemistry of the Upper and Lower Atmosphere; Academic Press: San Diego, CA, USA, 2000.
66	Miller J. A. ; Kee R. J. ; Westbrook C. K. Annu. Rev. Phys. Chem. 1990, 41, 345.
67	Kudla K. ; Schatz G. C. ; Wagner A. F. J. Chem. Phys. 1991, 95, 1635.
68	Bradley K. S. ; Schatz G. C. J. Chem. Phys. 1997, 106, 8464.
69	Yu H. G. ; Muckerman J. T. ; Sears T. J. Chem. Phys. Lett. 2001, 349, 547.
70	Lakin M. J. ; Troya D. ; Schatz G. C. ; Harding L. B. J. Chem. Phys. 2003, 119, 5848.
71	Valero R. ; van Hemert M. C. ; Kroes G. J. Chem. Phys. Lett. 2004, 393, 236.
72	Li J. ; Wang Y. ; Jiang B. ; Ma J. ; Dawes R. ; Xie D. ; Bowman J. M. ; Guo H. J. Chem. Phys. 2012, 136, 041103.
73	Jordan M. J. T. ; Gilbert R. G. J. Chem. Phys. 1995, 102, 5669.
74	Espinosa-García J. ; Corchado J. C. J. Phys. Chem. 1996, 100, 16561.
75	Wu T. ; Werner H.-J. ; Manthe U. Science 2004, 306, 2227.
76	Zhang X. ; Braams B. J. ; Bowman J. M. J. Chem. Phys. 2006, 124, 021104.
77	Xie Z. ; Bowman J. M. ; Zhang X. J. Chem. Phys. 2006, 125, 133120.
78	Agrafiotis D. K. ; Cede?o W. ; Lobanov V. S. J. Chem. Inform. Comput. Sci. 2002, 42, 903.
79	Chorkendorff, I. ; Niemantsverdriet, J. W. Concepts of Modern Catalysis and Kinetics; Wiley-VCH: Weinheim, Germany, 2003.
80	Jiang B. ; Xie D. ; Guo H. Chem. Sci. 2013, 4, 503.
81	Jiang B. ; Ren X. ; Xie D. ; Guo H. Proc. Natl. Acad. Sci. USA 2012, 109, 10224.
82	Ovesen C. V. ; Stoltze P. ; Norskov J. K. ; Campbell C. T. J. Catal. 1992, 134, 445.
83	Liu T. ; Zhang Z. ; Fu B. ; Yang X. ; Zhang D. H. Chem. Sci. 2016, 7, 1840.
84	Zhang Z. ; Liu T. ; Fu B. ; Yang X. ; Zhang D. H. Nat. Commun. 2016, 7, 11953.
85	Li J. ; Chen J. ; Zhang D. H. ; Guo H. J. Chem. Phys. 2014, 140, 044327.
86	Li J. ; Chen J. ; Zhao Z. ; Xie D. ; Zhang D. H. ; Guo H. J. Chem. Phys. 2015, 142, 204302.
87	Su N. Q. ; Chen J. ; Xu X. ; Zhang D. H. Acta Phys. -Chim. Sin. 2016, 32, 119.
87	苏乃强; 陈俊; 徐昕; 张东辉. 物理化学学报, 2016, 32, 119.
88	Su N. Q. ; Chen J. ; Sun Z. ; Zhang D. H. ; Xu X. J. Chem. Phys. 2015, 142, 084107.
89	Chen J. ; Su N. Q. ; Xu X. ; Zhang D. H. J. Comput. Chem. 2017, 38, 2326.
90	Decker, W. ; Greuel, G. -M. ; Pfister, G. ; Sch?nemann, H. Singular 4-0-2-A Computer Algebra System for Polynomial Computations. http://www.singular.uni-kl.de , 2015.
91	King S. A. J. Symb. Comput. 2013, 48, 101.
92	Chen J. ; Zhang D. H. Sci. China Chem. 2015, 45, 1241.
92	陈俊; 张东辉. 中国科学:化学, 2015, 45, 1241.

[1]	王晶晶, 曹贵强, 段瑞贤, 李向阳, 李喜飞. 金属单原子催化剂增强硫正极动力学的研究进展[J]. 物理化学学报, 2023, 39(5): 2212005 -0 .
[2]	许义飞, 杨瀚文, 常晓侠, 徐冰君. 电催化动力学简介[J]. 物理化学学报, 2023, 39(4): 2210025 -0 .
[3]	刘弘禹, 孟钢, 邓赞红, 李蒙, 常鋆青, 代甜甜, 方晓东. VOCs分子的半导体型传感器识别检测研究进展[J]. 物理化学学报, 2022, 38(5): 2008018 - .
[4]	邹竞祥,沈军,许恩华,方涛,黎书华. 基于块相关框架的多参考微扰理论和多参考耦合簇理论[J]. 物理化学学报, 2017, 33(7): 1277 -1287 .
[5]	张田雷,杨晨,凤旭凯,王竹青,王睿,刘秋丽,张鹏,王文亮. HS+HO₂气相反应机理及主通道速率常数的理论研究[J]. 物理化学学报, 2016, 32(3): 701 -710 .
[6]	汤焕丰,黄在银,肖明,梁敏,陈栎莹. 立方体纳米氧化亚铜反应动力学的理论及实验研究[J]. 物理化学学报, 2016, 32(12): 2891 -2897 .
[7]	苏乃强,陈俊,徐昕,张东辉. 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[J]. 物理化学学报, 2016, 32(1): 119 -130 .
[8]	郭庆,周传耀,马志博,任泽峰,樊红军,杨学明. TiO₂表面光催化基元过程[J]. 物理化学学报, 2016, 32(1): 28 -47 .
[9]	戴伟,陈柳杨,郑利敏,杨明晖. 应用多中心分区方法构建H₃分子反应势能面[J]. 物理化学学报, 2015, 31(11): 2077 -2082 .
[10]	赵雪婷, 董永春, 程博闻, 康卫民. 不同直径改性PAN纳米纤维膜与Fe³⁺的配位反应及其配合物对有机染料降解的催化性能[J]. 物理化学学报, 2013, 29(12): 2513 -2522 .
[11]	曾秀琳, 黄山奇松, 居学海. U⁺与CO₂气相反应的密度泛函理论研究[J]. 物理化学学报, 2013, 29(11): 2308 -2312 .
[12]	李佳, 徐雯丽, 胡静, 凌敏, 姚建华. 2,4-二氯苯氧乙酸代谢中的水解反应机理[J]. 物理化学学报, 2013, 29(09): 1923 -1930 .
[13]	李娜, 陈秋艳, 罗孟飞, 鲁继青. Pt/TiO₂催化剂上CO氧化反应动力学研究[J]. 物理化学学报, 2013, 29(05): 1055 -1062 .
[14]	鲍士龙, 陈网桦, 陈利平, 高海素, 吕家育. 2,4-二硝基甲苯热解自催化特性鉴别及其热解动力学[J]. 物理化学学报, 2013, 29(03): 479 -485 .
[15]	曹春晖, 林瑞, 赵天天, 黄真, 马建新. 用于燃料电池Co@Pt/C核壳结构催化剂的制备及表征[J]. 物理化学学报, 2013, 29(01): 95 -101 .

多原子反应体系的高精度拟合势能面

Highly Accurately Fitted Potential Energy Surfaces for Polyatomic Reactive Systems

RichHTML

PDF

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

推荐阅读 0