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Received September 6, 2020
Accepted November 2, 2020
- This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Deep learning-based initial guess for minimum energy path calculations
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
jihankim@kaist.ac.kr
Korean Journal of Chemical Engineering, February 2021, 38(2), 406-410(5), 10.1007/s11814-020-0704-1
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Abstract
An autoencoder that automatically generates an initial guess for the minimum energy pathway (MEP) calculations has been designed. Specifically, our autoencoder takes in the trajectories of molecular dynamics simulations as its input and facilitates the generation of feasible molecular coordinates. Two molecules (acetonitrile and alanine dipeptide) were tested using the nudged elastic band calculations and the results provided improvements over linear interpolation and image dependent pair potential methods in terms of the number of SCF iterations, demonstrating the utility of using an autoencoder type of an approach for MEP calculations.
Keywords
References
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Govind N, Petersen M, Fitzgerald G, King-Smith D, Andzelm J, Comput. Mater. Sci., 28, 250 (2003)
Smidstrup S, Pedersen A, Stokbro K, Jonsson H, J. Chem. Phys., 140, 214106 (2014)
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Wang LP, Titov A, McGibbon R, Liu F, Pande VS, Martinez TJ, Nat. Chem., 6, 1044 (2014)
Wang LP, McGibbon RT, Pande VS, Martinez TJ, J. Chem. Theory Comput., 12, 638 (2016)
Dewyer AL, Arguelles AJ, Zimmerman PM, Wiley Interdiscip. Rev. Comput. Mol. Sci., 8, 1 (2018).
Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P, Adv. Neural Inf. Process. Syst., 2180 (2016).
Upchurch P, et al., Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 2017-Janua, 6090 (2017).
Berthelot D, Goodfellow I, Raffel C, Roy A, 7th Int. Conf. Learn. Represent. ICLR 2019 (2019).
Kramer MA, AIChE J., 37, 233 (1991)
Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA, J. Mach. Learn. Res., 11, 3371 (2010)
Kingma DP, Welling M, 2nd Int. Conf. Learn. Represent. ICLR 2014 - Conf. Track Proc., 1 (2014).
Plimpton S, J. Comput. Phys., 117, 1 (1997)
Plimpton S, Thomson AP, MRS Bulletin, 37, 513 (2012)
Rappe AK, Casewit CJ, Colwell KS, Goddard WA, Skiff WM, J. Am. Chem. Soc., 114, 10024 (1992)
Kresse G, Hafner J, Phys. Rev. B, 48, 13115 (1993)
Kresse G, Furthmuller J, Hafner J, Phys. Rev. B, 50, 13181 (1994)
Kresse G, Furthmuller J, Phys. Rev. B, 54, 11169 (1996)
Blochl PE, Phys. Rev. B, 50, 17953 (1994)
Perdew JP, Burke K, Ernzerhof M, Phys. Rev. Lett., 77, 3865 (1996)
Bitzek E, Koskinen P, Gahler F, Moseler M, Gumbsch P, Phys. Rev. Lett., 97, 1 (2006)
Henkelman G, Uberuaga BP, Jonsson H, J. Chem. Phys., 113(22), 9901 (2000)
Larsen A, Mortensen JJ, Blomqvist J, Castelli I, Christensen R, Dułak M, Friis J, Groves MN, Hammer B, Hargus C, J. Phys. Condens. Matter, 29, 273002 (2017)
Ren W, Vanden-Eijnden E, Maragakis P, Weinan E, J. Chem. Phys., 123, 134109 (2005)
Allouche A, J. Comput. Chem., 32, 174 (2012)
Bolhuis PG, Dellago C, Chandler D, Proc. Natl. Acad. Sci. U.S.A., 97, 5877 (2000)