화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Chemical Engineering, Vol.38, No.10, 1971-1982, October, 2021
DOI10.1007/s11814-021-0869-2  Export Citation
Machine learning-based discovery of molecules, crystals, and composites: A perspective review
Sangwon Lee, Haeun Byun, Mujin Cheon, Jihan Kim†, and Jay Hyung Lee†
  • Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
E-mail:,
Machine learning based approaches to material discovery are reviewed with the aim of providing a perspective on the current state of the art and its potential. Various models used to represent molecules and crystals are introduced and such representations can be used within the neural networks to generate materials that satisfy specified physical features and properties. For problems where large database for structure-property map cannot be created, the active learning approaches based on Bayesian optimization to maximize the efficiency of a search are reviewed. Successful applications of these machine learning based material discovery approaches are beginning to appear and some of the notable ones are reviewed.
Keywords:Material Discovery;Machine Learning;Molecule Design;Crystal Design;Adaptive Experimental Design;Bayesian Optimization
  1. LeCun Y, Bengio Y, Hinton G, Nature, 521(7553), 436 (2015)
  2. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D, Nature, 518(7540), 529 (2015)
  3. Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S, Grewe D, Nham J, Kalchbrenner N, Sutskever I, Lillicrap T, Leach M, Kavukcuoglu K, Graepel T, Hassabis D, Nature, 529(7587), 484 (2016)
  4. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A, Nature, 559(7715), 547 (2018)
  5. Agrawal A, Choudhary A, APL Materials, 4, 053208 (2016)
  6. Rupp M, Tkatchenko A, Muller KR, von Lilienfeld OA, Phys. Rev. Lett., 108, 058301 (2012)
  7. Hastie T, Tibshirani R, Friedman J, The elements of statistical learning, Springer, New York (2009).
  8. Hansen K, Biegler F, Ramakrishnan R, Pronobis W, von Lilienfeld OA, Muller KR, Tkatchenko A, J. Phys. Chem. Lett., 6, 2326 (2015)
  9. Weininger D, J. Chem. Information Modeling, 28, 31 (1988)
  10. Weininger D, Weininger A, Weininger JL, J. Chem. Information Modeling, 29, 97 (1989)
  11. Kearnes S, McCloskey K, Berndl M, Pande V, Riley P, J. Comput.-Aided Mol. Des., 30, 595 (2016)
  12. Duvenaud D, et al., arXiv preprint arXiv:1509.09292 (2015).
  13. Garcia-Garcia A, et al., arXiv preprint arXiv:1704.06857 (2017).
  14. Zhou J, Cui G, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M, AI Open, 1, 57 (2020)
  15. Bartok AP, Kondor R, Csanyi G, Phys. Rev. B, 87, 184115 (2013)
  16. von Lilienfeld OA, Ramakrishnan R, Rupp M, Knoll A, Int. J. Quantum Chem., 115, 1084 (2015)
  17. Valle M, Oganov AR, Acta Crystallogr. Sect. A, 66, 507 (2010)
  18. Schutt KT, Glawe H, Brockherde F, Sanna A, Muller KR, Gross EKU, Phys. Rev. B, 89, 205118 (2014)
  19. Faber F, Lindmaa A, von Lilienfeld OA, Armiento R, Int. J. Quantum Chem., 115, 1094 (2015)
  20. Xie T, Grossman JC, Phys. Rev. Lett., 120, 145301 (2018)
  21. Behler J, Parrinello M, Phys. Rev. Lett., 98, 146401 (2007)
  22. Behler J, J. Chem. Phys., 134, 074106 (2011)
  23. Behler J, Int. J. Quantum Chem., 115, 1032 (2015)
  24. Smith JS, Isayev O, Roitberg AE, Chem. Sci., 8, 3192 (2017)
  25. Gastegger M, Schwiedrzik L, Bittermann M, Berzsenyi F, Marquetand P, J. Chem. Phys., 148, 241709 (2018)
  26. Schutt KT, Sauceda HE, Kindermans PJ, Tkatchenko A, Muller KR, J. Chem. Phys., 148, 241722 (2018)
  27. Schutt KT, Kessel P, Gastegger M, Nicoli KA, Tkatchenko A, Muller KR, J. Chem. Theory Comput., 15(1), 448 (2018)
  28. Lin LC, Berger AH, Martin RL, Kim J, Swisher JA, Jariwala K, Rycroft CH, Bhown AS, Deem M, Haranczyk M, Smit B, Nat. Mater., 11(7), 633 (2012)
  29. Wilmer CE, Leaf M, Lee CY, Farha OK, Hauser BG, Hupp JT, Snurr RQ, Nat. Chem., 4, 83 (2012)
  30. Gomez-Gualdron DA, Wilmer CE, Farha OK, Hupp JT, Snurr RQ, J. Phys. Chem. C, 118, 6941 (2014)
  31. Simon CM, Kim J, Gomez-Gualdron DA, Camp JS, et al., Energy Environ. Sci., 8, 1190 (2015)
  32. Mullard A, Nature, 549(7673), 445 (2017)
  33. Sanchez-Lengeling B, Aspuru-Guzik A, Science, 361(6400), 360 (2018)
  34. Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y, Commun. ACM, 63(11), 139 (2020)
  35. Kingma DP, Welling M, arXiv preprint arXiv:1312.6114 (2013).
  36. Gomez-Bombarelli R, Wei JN, Duvenaud D, et al., ACS Cent. Sci., 4, 268 (2018)
  37. Kusner MJ, Paige B, Hernandez-Lobato JM, ICML, PMLR (2017).
  38. Putin E, Asadulaev A, Ivanenkov Y, Aladinskiy V, Sanchez-Lengeling B, Aspuru-Gzik A, Zhavoronkov A, J. Chem. Information Modeling, 58, 1194 (2018)
  39. Segler MHS, Kogej T, Tyrchan C, Waller MP, ACS Cent. Sci., 4, 120 (2018)
  40. Guimaraes GL, et al., arXiv preprint arXiv:1705.10843 (2017).
  41. Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A, Mol. Pharm., 14, 3098 (2017)
  42. Olivecrona M, Blaschke T, Engkvist O, Chen H, J. Cheminformatics, 9, 48 (2017)
  43. De Cao N, Kipf T, arXiv preprint arXiv:1805.11973 (2018).
  44. Gebauer NWA, Gastegger M, Schutt KT, arXiv preprint arXiv:1810.11347 (2018).
  45. Xue D, Gong Y, Yang Z, Chuai G, Qu S, Shen A, Yu J, Liu Q, Wiley Interdiscip. Rev. Comput. Mol. Sci., 9, e1395 (2018)
  46. Li Y, Zhang L, Liu Z, J. Cheminformatics, 10(1), 1 (2018)
  47. Simonovsky M, Komodakis N, ICANN, Springer, Cham (2018).
  48. Zhou Q, Tang P, Liu S, Pan J, Yan Q, Zhang SC, Proc. Natl. Acad. Sci., 115(28), E6411 (2018)
  49. Ziletti A, Kumar D, Scheffler M, Ghiringhelli LM, Nat. Commun., 9, 2775 (2018)
  50. Noh J, Kim J, Stein HS, Sanchez-Lengeling B, Gregoire JM, Aspuru-Guzik A, Jung Y, Matter, 1(5), 1370 (2019)
  51. Kim S, Noh J, Gu GH, Aspuru-Guzik A, Jung Y, ACS Cent. Sci., 6, 1412 (2020)
  52. Jang J, Gu GH, Noh J, Kim J, Jung Y, J. Am. Chem. Soc., 142, 18836 (2020)
  53. Bobbitt NS, Snurr RQ, Mol. Simul., 45(14-15), 1069 (2019)
  54. Fernandez M, Boyd PG, Daff TD, Aghaji MZ, Woo TK, J. Phys. Chem. Lett., 5, 3056 (2014)
  55. Simon CM, Mercado R, Schnell SK, Smit B, Haranczyk M, Chem. Mater., 27, 4459 (2015)
  56. Chung YG, Gomez-Gualdron DA, Li P, Leperi KT, et al., Sci. Adv., 2(10), e16009 (2016)
  57. Raza A, Sturluson A, Simon CM, Fern X, J. Phys. Chem. C, 124, 19070 (2020)
  58. Yao Z, Sanchez-Lengeling B, Bobbitt NS, Bucior BJ, et al., Nat. Mach. Intell., 3, 76 (2021)
  59. Lee S, Kim B, Kim J, J. Mater. Chem. A, 7, 2709 (2019)
  60. Kim B, Lee S, Kim J, Sci. Adv., 6, eaax93 (2020)
  61. Xue D, Balachandran PV, Hogden J, Theiler J, Xue D, Lookman T, Nat. Commun., 7(1), 1 (2016)
  62. Forrester AIJ, Keane AJ, Prog. Aerosp. Sci., 45, 50 (2009)
  63. Raccuglia P, Elbert KC, Adler PDF, Falk C, Wenny MB, Mollo A, Zeller M, Friedler SA, Schrier J, Norquist AJ, Nature, 533(7601), 73 (2016)
  64. Pruksawan S, Lambard G, Samitsu S, Sodeyama K, Naito M, Sci. Technol. Adv. Mater., 20, 1010 (2019)
  65. Mockus J, J. Glob. Optim., 4, 347 (1994)
  66. Jones DR, Schonlau M, Welch WJ, J. Glob. Optim., 13, 455 (1998)
  67. Streltsov S, Vakili P, J. Glob. Optim., 14, 283 (1999)
  68. Rasmussen CE, Williams C, Gaussian processes for machine learning, MIT Press, Cambridge (2006).
  69. Jones DR, Schonlau M, Welch WJ, J. Glob. Optim., 13, 455 (1998)
  70. Frazier PI, Powell WB, Dayanik S, SICON, 47, 2410 (2008)
  71. Knowles J, IEEE Trans. Evol. Comput., 10, 50 (2006)
  72. Das I, Nonlinear multicriteria optimization and robust optimality, Rice University (1997).
  73. Ponweiser W, et al., Multiobjective optimization on a limited budget of evaluations using modelassisted S-metric selection, Springer, Berlin (2008).
  74. Zuluaga M, Sergent G, Krause A, Puschel M, ICML, PMLR (2013).
  75. Emmerich M, Klinkenberg JW, Rapport technique, Leiden University, 34, 7 (2008).
  76. Picheny V, Stat. Comput., 25, 1265 (2015)
  77. Hernandez-Lobato D, Hernandez-Lobato J, Shah A, Adams R, ICML, PMLR (2016).
  78. Schonlau M, Computer experiments and global optimization, University of Waterloo (1997).
  79. Sasena MJ, Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations, University of Michigan (2002).
  80. Sasena M, Papalambros P, Goovaerts P, 8th Multidiscip. Anal. Optim. Conf., 4921 (2000).
  81. Audet C, Denni J, Moore D, Booker A, Frank P, 8th Multidiscip. Anal. Optim. Conf., 4891 (2000).
  82. Bichon B, Mahadevan S, Eldred M, 50th AIAA/ASCE/AHS/ASC Struct. Struct. Dyn. Mater. Conf. (2009).
  83. Picheny V, Gramacy RB, Wild S, Digabel SL, ICONIP, 1443 (2016).
  84. Lee H, Gramacy R, Linkletter C, Gray G, Pac. J. Optim., 7, 467 (2011)
  85. Basudhar A, Dribusch C, Lacaze S, Missoum S, Struct. Multidiscip., 46, 201 (2012)
  86. Azimi J, Fern A, Fern XZ, NeurIPS (2010).
  87. Bergstra J, Bardenet R, Bengio Y, Kegl B, NeurIPS, 24 (2011).
  88. Azimi J, Jalali A, Fern X, arXiv preprint arXiv:1202.5597 (2012).
  89. Schonlau M, Welch WJ, Jones DR, Lecture Notes-Monograph Series, 34, 11 (1998).
  90. Contal E, Buffoni D, Robicquet A, Vayatis N, ECML PKDD, 225 (2013).
  91. Desautels T, Krause A, Burdick JW, J. Mach. Learn. Res., 15, 3873 (2014)
  92. Ocenasek J, Schwarz J, The state of the art in computational intelligence, 61, Physica, Heidelberg (2000).
  93. Taddy MA, Lee HKH, Gray GA, Griffin JD, Technometrics, 51, 389 (2009)
  94. Schmidt J, Marques MRG, Botti S, Marques MAL, Npj Comput. Mater., 5, 1 (2019)
  95. Lookman T, Balachandran PV, Xue D, Hogden J, Theiler J, Curr. Opin. Solid State Mater. Sci., 21, 121 (2017)
  96. Balachandran PV, Xue D, Theiler J, Hogden J, Lookman T, Sci. Rep., 6, 1 (2016)
  97. Talapatra A, Boluki S, Duong T, Qian X, Dougherty E, Arroyave R, Phys. Rev. Mater., 2, 113803 (2018)
  98. Griffiths RR, Hernandez-Lobato JM, arXiv preprint arXiv: 1709.05501 (2017).