Issue
Korean Journal of Chemical Engineering,
Vol.38, No.2, 406-410, 2021
Deep learning-based initial guess for minimum energy path calculations
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.
[References]
  1. Fukui K, Accounts Chem. Res., 14, 363, 1981
  2. Schlegel HB, J. Comput. Chem., 24, 1514, 2003
  3. Laidler LK. King MC, J. Phys. Chem., 87, 2657, 1983
  4. Pratt LR, J. Chem. Phys., 85, 5045, 1986
  5. Elber R, Karplus M, Chem. Phys. Lett., 139, 375, 1987
  6. Weinan E, Ren W, Vanden-Eijnden E, Phys. Rev. B, 66, 523011, 2002
  7. Weinan E, Ren W, Vanden-Eijnden E, J. Chem. Phys., 126, 164103, 2017
  8. Jonsson H, et al., in Classical and quantum dynamics in condensed phase simulations, World Scientific, Singapore (1998).
  9. Sheppard D, Xiao P, Chemelewski W, Johnson DD, Henkelman G, J. Chem. Phys., 136, 074103, 2012
  10. Henkelman G, Jonsson H, J. Chem. Phys., 113(22), 9978, 2000
  11. Sheppard D, Trrell R, Henkelman G, J. Chem. Phys., 128, 1, 2008
  12. Herbol HC, Stevenson J, Clancy P, J. Chem. Theory Comput, 13, 3250, 2017
  13. Raber LR, Chem. Eng. News, 75, 39, 1997
  14. Govind N, Petersen M, Fitzgerald G, King-Smith D, Andzelm J, Comput. Mater. Sci., 28, 250, 2003
  15. Smidstrup S, Pedersen A, Stokbro K, Jonsson H, J. Chem. Phys., 140, 214106, 2014
  16. Martinez-Nunez E, J. Comput. Chem., 36, 222, 2015
  17. Wang LP, Titov A, McGibbon R, Liu F, Pande VS, Martinez TJ, Nat. Chem., 6, 1044, 2014
  18. Wang LP, McGibbon RT, Pande VS, Martinez TJ, J. Chem. Theory Comput., 12, 638, 2016
  19. Dewyer AL, Arguelles AJ, Zimmerman PM, Wiley Interdiscip. Rev. Comput. Mol. Sci., 8, 1 (2018).
  20. Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I, Abbeel P, Adv. Neural Inf. Process. Syst., 2180 (2016).
  21. Upchurch P, et al., Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 2017-Janua, 6090 (2017).
  22. Berthelot D, Goodfellow I, Raffel C, Roy A, 7th Int. Conf. Learn. Represent. ICLR 2019 (2019).
  23. Kramer MA, AIChE J., 37, 233, 1991
  24. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA, J. Mach. Learn. Res., 11, 3371, 2010
  25. Kingma DP, Welling M, 2nd Int. Conf. Learn. Represent. ICLR 2014 - Conf. Track Proc., 1 (2014).
  26. Plimpton S, J. Comput. Phys., 117, 1, 1997
  27. Plimpton S, Thomson AP, MRS Bulletin, 37, 513, 2012
  28. Rappe AK, Casewit CJ, Colwell KS, Goddard WA, Skiff WM, J. Am. Chem. Soc., 114, 10024, 1992
  29. Kresse G, Hafner J, Phys. Rev. B, 48, 13115, 1993
  30. Kresse G, Furthmuller J, Hafner J, Phys. Rev. B, 50, 13181, 1994
  31. Kresse G, Furthmuller J, Phys. Rev. B, 54, 11169, 1996
  32. Blochl PE, Phys. Rev. B, 50, 17953, 1994
  33. Perdew JP, Burke K, Ernzerhof M, Phys. Rev. Lett., 77, 3865, 1996
  34. Bitzek E, Koskinen P, Gahler F, Moseler M, Gumbsch P, Phys. Rev. Lett., 97, 1, 2006
  35. Henkelman G, Uberuaga BP, Jonsson H, J. Chem. Phys., 113(22), 9901, 2000
  36. 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
  37. Ren W, Vanden-Eijnden E, Maragakis P, Weinan E, J. Chem. Phys., 123, 134109, 2005
  38. Allouche A, J. Comput. Chem., 32, 174, 2012
  39. Bolhuis PG, Dellago C, Chandler D, Proc. Natl. Acad. Sci. U.S.A., 97, 5877, 2000