1932

Abstract

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward () the discovery of new materials through large-scale enumerative screening, () the design of materials through identification of rules and principles that govern materials properties, and () the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-chembioeng-092320-120230
2022-06-07
2024-10-10
Loading full text...

Full text loading...

/deliver/fulltext/chembioeng/13/1/annurev-chembioeng-092320-120230.html?itemId=/content/journals/10.1146/annurev-chembioeng-092320-120230&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Curtarolo S, Hart GLW, Nardelli MB, Mingo N, Sanvito S, Levy O. 2013. The high-throughput highway to computational materials design. Nat. Mater. 12:191–201
    [Google Scholar]
  2. 2.
    Jain A, Ong SP, Hautier G, Chen W, Richards WD et al. 2013. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1:011002
    [Google Scholar]
  3. 3.
    Agrawal A, Choudhary A. 2016. Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL Mater. 4:053208
    [Google Scholar]
  4. 4.
    Dimitrov T, Kreisbeck C, Becker JS, Aspuru-Guzik A, Saikin SK. 2019. Autonomous molecular design: then and now. ACS Appl. Mater. Interfaces 11:24825–36
    [Google Scholar]
  5. 5.
    Burello E, Rothenberg G. 2003. Optimal Heck cross-coupling catalysis: a pseudo-pharmaceutical approach. Adv. Synth. Catal. 345:1334–40
    [Google Scholar]
  6. 6.
    Burello E, Farrusseng D, Rothenberg G. 2004. Combinatorial explosion in homogeneous catalysis: screening 60,000 cross-coupling reactions. Adv. Synth. Catal. 346:1844–53
    [Google Scholar]
  7. 7.
    Burello E, Rothenberg G. 2005. Topological mapping of bidentate ligands: a fast approach for screening homogeneous catalysts. Adv. Synth. Catal. 347:1969–77
    [Google Scholar]
  8. 8.
    Landrum GA, Penzotti JE, Putta S. 2005. Machine-learning models for combinatorial catalyst discovery. Meas. Sci. Technol. 16:270–77
    [Google Scholar]
  9. 9.
    Ramakrishnan R, Dral PO, Rupp M, von Lilienfeld OA 2014. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 1:140022
    [Google Scholar]
  10. 10.
    Wu ZQ, Ramsundar B, Feinberg EN, Gomes J, Geniesse C et al. 2018. MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9:513–30
    [Google Scholar]
  11. 11.
    Haghighatlari M, Vishwakarma G, Altarawy D, Subramanian R, Kota BU et al. 2020. ChemML: a machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data. WIREs Comput. Mol. Sci. 10:e1458
    [Google Scholar]
  12. 12.
    Huo H, Rong Z, Kononova O, Sun W, Botari T et al. 2019. Semi-supervised machine-learning classification of materials synthesis procedures. npj Comput. Mater. 5:62
    [Google Scholar]
  13. 13.
    Kim E, Huang K, Jegelka S, Olivetti E. 2017. Virtual screening of inorganic materials synthesis parameters with deep learning. npj Comput. Mater. 3:53
    [Google Scholar]
  14. 14.
    Kim E, Huang K, Saunders A, McCallum A, Ceder G, Olivetti E. 2017. Materials synthesis insights from scientific literature via text extraction and machine learning. Chem. Mater. 29:9436–44
    [Google Scholar]
  15. 15.
    Kononova O, He T, Huo H, Trewartha A, Olivetti EA, Ceder G. 2021. Opportunities and challenges of text mining in materials research. iScience 24:102155
    [Google Scholar]
  16. 16.
    Moosavi SM, Chidambaram A, Talirz L, Haranczyk M, Stylianou KC, Smit B. 2019. Capturing chemical intuition in synthesis of metal-organic frameworks. Nat. Commun. 10:539
    [Google Scholar]
  17. 17.
    Nandy A, Duan C, Kulik HJ. 2021. Using machine learning and data mining to leverage community knowledge for the engineering of stable metal-organic frameworks. J. Am. Chem. Soc. 143:4217535–47
    [Google Scholar]
  18. 18.
    Nandy A, Duan C, Taylor MG, Liu F, Steeves AH, Kulik HJ. 2021. Computational discovery of transition-metal complexes: from high-throughput screening to machine learning. Chem. Rev. 121:9927–10000
    [Google Scholar]
  19. 19.
    Swain MC, Cole JM. 2016. ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature. J. Chem. Inf. Model. 56:1894–904
    [Google Scholar]
  20. 20.
    Foscato M, Venkatraman V, Occhipinti G, Alsberg BK, Jensen VR. 2014. Automated building of organometallic complexes from 3D fragments. J. Chem. Inf. Model. 54:1919–31
    [Google Scholar]
  21. 21.
    Ioannidis EI, Gani TZH, Kulik HJ. 2016. molSimplify: a toolkit for automating discovery in inorganic chemistry. J. Comput. Chem. 37:2106–17
    [Google Scholar]
  22. 22.
    Nandy A, Duan C, Janet JP, Gugler S, Kulik HJ. 2018. Strategies and software for machine learning accelerated discovery in transition metal chemistry. Ind. Eng. Chem. Res. 57:13973–86
    [Google Scholar]
  23. 23.
    Ward L, Dunn A, Faghaninia A, Zimmermann NER, Bajaj S et al. 2018. Matminer: an open source toolkit for materials data mining. Comput. Mater. Sci. 152:60–69
    [Google Scholar]
  24. 24.
    Ward L, Agrawal A, Choudhary A, Wolverton C. 2016. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput. Mater. 2:16028
    [Google Scholar]
  25. 25.
    Groom CR, Bruno IJ, Lightfoot MP, Ward SC. 2016. The Cambridge structural database. Acta Crystallogr. B 72:171–79
    [Google Scholar]
  26. 26.
    Gugler S, Janet JP, Kulik HJ 2020. Enumeration of de novo inorganic complexes for chemical discovery and machine learning. Mol. Syst. Des. Eng. 5:139–52
    [Google Scholar]
  27. 27.
    Wilmer CE, Leaf M, Lee CY, Farha OK, Hauser BG et al. 2012. Large-scale screening of hypothetical metal–organic frameworks. Nat. Chem. 4:83
    [Google Scholar]
  28. 28.
    Colón YJ, Gómez-Gualdrón DA, Snurr RQ. 2017. Topologically guided, automated construction of metal–organic frameworks and their evaluation for energy-related applications. Cryst. Growth Des. 17:5801–10
    [Google Scholar]
  29. 29.
    Chung YG, Haldoupis E, Bucior BJ, Haranczyk M, Lee S et al. 2019. Advances, updates, and analytics for the computation-ready, experimental metal–organic framework database: CoRE MOF 2019. J. Chem. Eng. Data 64:5985–98
    [Google Scholar]
  30. 30.
    Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C. 2013. Materials design and discovery with high-throughput density functional theory: the Open Quantum Materials Database (OQMD). JOM 65:1501–9
    [Google Scholar]
  31. 31.
    Balcells D, Skjelstad BB. 2020. tmQM Dataset—quantum geometries and properties of 86k transition metal complexes. J. Chem. Inf. Model. 60:6135–46
    [Google Scholar]
  32. 32.
    Janet JP, Kulik HJ. 2017. Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships. J. Phys. Chem. A 121:8939–54
    [Google Scholar]
  33. 33.
    Janet JP, Kulik HJ. 2017. Predicting electronic structure properties of transition metal complexes with neural networks. Chem. Sci. 8:5137–52
    [Google Scholar]
  34. 34.
    Meredig B, Agrawal A, Kirklin S, Saal JE, Doak JW et al. 2014. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B 89:094104
    [Google Scholar]
  35. 35.
    Ruddigkeit L, van Deursen R, Blum LC, Reymond J-L. 2012. Enumeration of 166 billion organic small molecules in the Chemical Universe Database GDB-17. J. Chem. Inf. Model. 52:2864–75
    [Google Scholar]
  36. 36.
    Foscato M, Houghton BJ, Occhipinti G, Deeth RJ, Jensen VR. 2015. Ring closure to form metal chelates in 3D fragment-based de novo design. J. Chem. Inf. Model. 55:1844–56
    [Google Scholar]
  37. 37.
    Janet JP, Ramesh S, Duan C, Kulik HJ. 2020. Accurate multiobjective design in a space of millions of transition metal complexes with neural-network-driven efficient global optimization. ACS Cent. Sci. 6:513–24
    [Google Scholar]
  38. 38.
    Boyd PG, Woo TK. 2016. A generalized method for constructing hypothetical nanoporous materials of any net topology from graph theory. CrystEngComm 18:3777–92
    [Google Scholar]
  39. 39.
    Martin RL, Haranczyk M. 2014. Construction and characterization of structure models of crystalline porous polymers. Cryst. Growth Des. 14:2431–40
    [Google Scholar]
  40. 40.
    Moosavi SM, Nandy A, Jablonka KM, Ongari D, Janet JP et al. 2020. Understanding the diversity of the metal-organic frameworks ecosystems. Nat. Commun. 11:4068
    [Google Scholar]
  41. 41.
    Deem MW, Pophale R, Cheeseman PA, Earl DJ. 2009. Computational discovery of new zeolite-like materials. J. Phys. Chem. C 113:21353–60
    [Google Scholar]
  42. 42.
    Hageman JA, Westerhuis JA, Frühauf H-W, Rothenberg G. 2006. Design and assembly of virtual homogeneous catalyst libraries—towards in silico catalyst optimisation. Adv. Synth. Catal. 348:361–69
    [Google Scholar]
  43. 43.
    Nandy A, Zhu J, Janet JP, Duan C, Getman RB, Kulik HJ. 2019. Machine learning accelerates the discovery of design rules and exceptions in stable metal-oxo intermediate formation. ACS Catal 9:8243–55
    [Google Scholar]
  44. 44.
    Liu F, Duan C, Kulik HJ. 2020. Rapid detection of strong correlation with machine learning for transition-metal complex high-throughput screening. J. Phys. Chem. Lett. 11:8067–76
    [Google Scholar]
  45. 45.
    Ramos-Cordoba E, Matito E. 2017. Local descriptors of dynamic and nondynamic correlation. J. Chem. Theory Comput. 13:2705–11
    [Google Scholar]
  46. 46.
    Janet JP, Duan C, Yang T, Nandy A, Kulik HJ. 2019. A quantitative uncertainty metric controls error in neural network-driven chemical discovery. Chem. Sci. 10:7913–22
    [Google Scholar]
  47. 47.
    Gómez-Gualdrón DA, Colón YJ, Zhang X, Wang TC, Chen Y-S et al. 2016. Evaluating topologically diverse metal–organic frameworks for cryo-adsorbed hydrogen storage. Energy Environ. Sci. 9:3279–89
    [Google Scholar]
  48. 48.
    Martin RL, Simon CM, Smit B, Haranczyk M. 2014. In silico design of porous polymer networks: high-throughput screening for methane storage materials. J. Am. Chem. Soc. 136:5006–22
    [Google Scholar]
  49. 49.
    Duan C, Janet JP, Liu F, Nandy A, Kulik HJ. 2019. Learning from failure: predicting electronic structure calculation outcomes with machine learning models. J. Chem. Theory Comput. 15:2331–45
    [Google Scholar]
  50. 50.
    Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel TD, Duvenaud D, Maclaurin D et al. 2016. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15:1120–27
    [Google Scholar]
  51. 51.
    Boyd PG, Chidambaram A, García-Díez E, Ireland CP, Daff TD et al. 2019. Data-driven design of metal–organic frameworks for wet flue gas CO2 capture. Nature 576:253–56
    [Google Scholar]
  52. 52.
    Janet JP, Liu F, Nandy A, Duan C, Yang T et al. 2019. Designing in the face of uncertainty: exploiting electronic structure and machine learning models for discovery in inorganic chemistry. Inorg. Chem. 58:10592–606
    [Google Scholar]
  53. 53.
    Durand DJ, Fey N 2019. Computational ligand descriptors for catalyst design. Chem. Rev. 119:6561–94
    [Google Scholar]
  54. 54.
    Mansson RA, Welsh AH, Fey N, Orpen AG. 2006. Statistical modeling of a ligand knowledge base. J. Chem. Inf. Model. 46:2591–600
    [Google Scholar]
  55. 55.
    Beyreuther S, Hunger J, Huttner G, Mann S, Zsolnai L. 1996. Conformation of tripod metal templates in CH3C(CH2PPh2)3MLn (n = 2, 3): neural networks in conformational analysis. Chem. Ber. 129:745–57
    [Google Scholar]
  56. 56.
    Ceriotti M. 2019. Unsupervised machine learning in atomistic simulations, between predictions and understanding. J. Chem. Phys. 150:150901
    [Google Scholar]
  57. 57.
    Virshup AM, Contreras-García J, Wipf P, Yang W, Beratan DN 2013. Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds. J. Am. Chem. Soc. 135:7296–303
    [Google Scholar]
  58. 58.
    Cruz VL, Martinez S, Ramos J, Martinez-Salazar J. 2014. 3D-QSAR as a tool for understanding and improving single-site polymerization catalysts. A review. Organometallics 33:2944–59
    [Google Scholar]
  59. 59.
    Sigman MS, Harper KC, Bess EN, Milo A 2016. The development of multidimensional analysis tools for asymmetric catalysis and beyond. Acc. Chem. Res. 49:1292–301
    [Google Scholar]
  60. 60.
    Zahrt AF, Athavale SV, Denmark SE. 2020. Quantitative structure–selectivity relationships in enantio-selective catalysis: past, present, and future. Chem. Rev. 120:1620–89
    [Google Scholar]
  61. 61.
    Ahneman DT, Estrada JG, Lin S, Dreher SD, Doyle AG. 2018. Predicting reaction performance in C–N cross-coupling using machine learning. Science 360:186–90
    [Google Scholar]
  62. 62.
    Lundberg SM, Lee S-I 2017. A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems U von Luxburg, I Guyon, S Bengio, H Wallach, R Fergus 4768–77 New York: Assoc. Comput. Mach.
    [Google Scholar]
  63. 63.
    Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision618–26 Piscataway, NJ: IEEE
    [Google Scholar]
  64. 64.
    Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B et al. 2018. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4:268–76
    [Google Scholar]
  65. 65.
    Iovanac NC, Savoie BM. 2019. Improved chemical prediction from scarce data sets via latent space enrichment. J. Phys. Chem. A 123:4295–302
    [Google Scholar]
  66. 66.
    Cundari TR, Sârbu C, Pop HF. 2002. Robust fuzzy principal component analysis (FPCA). A comparative study concerning interaction of carbon−hydrogen bonds with molybdenum−oxo bonds. J. Chem. Inf. Comput. Sci. 42:1363–69
    [Google Scholar]
  67. 67.
    Saadun AJ, Pablo-García S, Paunović V, Li Q, Sabadell-Rendón A et al. 2020. Performance of metal-catalyzed hydrodebromination of dibromomethane analyzed by descriptors derived from statistical learning. ACS Catal 10:6129–43
    [Google Scholar]
  68. 68.
    van der Maaten L, Hinton G. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:2579–605
    [Google Scholar]
  69. 69.
    McInnes L, Healy J, Melville J. 2018. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 [stat.ML]
  70. 70.
    Maley Steven M, Kwon D-H, Rollins N, Stanley JC, Sydora OL et al. 2020. Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization. Chem. Sci. 11:9665–74
    [Google Scholar]
  71. 71.
    Martínez S, Cruz VL, Ramos J, Martínez-Salazar J. 2012. Polymerization activity prediction of zirconocene single-site catalysts using 3D quantitative structure–activity relationship modeling. Organometallics 31:1673–79
    [Google Scholar]
  72. 72.
    Friederich P, dos Passos Gomes G, De Bin R, Aspuru-Guzik A, Balcells D 2020. Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex. Chem. Sci. 11:4584–601
    [Google Scholar]
  73. 73.
    Cordova M, Wodrich MD, Meyer B, Sawatlon B, Corminboeuf C. 2020. Data-driven advancement of homogeneous nickel catalyst activity for aryl ether cleavage. ACS Catal 10:7021–31
    [Google Scholar]
  74. 74.
    Meyer B, Sawatlon B, Heinen S, von Lilienfeld OA, Corminboeuf C. 2018. Machine learning meets volcano plots: computational discovery of cross-coupling catalysts. Chem. Sci. 9:7069–77
    [Google Scholar]
  75. 75.
    Freeze JG, Kelly HR, Batista VS. 2019. Search for catalysts by inverse design: artificial intelligence, mountain climbers, and alchemists. Chem. Rev. 119:6595–612
    [Google Scholar]
  76. 76.
    Orpen A. 2002. Applications of the Cambridge Structural Database to molecular inorganic chemistry. Acta Crystallogr. B 58:398–406
    [Google Scholar]
  77. 77.
    Tolman CA. 1970. Phosphorus ligand exchange equilibriums on zerovalent nickel. A dominant role for steric effects. J. Am. Chem. Soc. 92:2956–65
    [Google Scholar]
  78. 78.
    Tolman CA. 1977. Steric effects of phosphorus ligands in organometallic chemistry and homogeneous catalysis. Chem. Rev. 77:313–48
    [Google Scholar]
  79. 79.
    Falivene L, Cao Z, Petta A, Serra L, Poater A et al. 2019. Towards the online computer-aided design of catalytic pockets. Nat. Chem. 11:872–79
    [Google Scholar]
  80. 80.
    Bucior BJ, Bobbitt NS, Islamoglu T, Goswami S, Gopalan A et al. 2019. Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks. Mol. Syst. Des. Eng. 4:162–74
    [Google Scholar]
  81. 81.
    Zahrt AF, Henle JJ, Rose BT, Wang Y, Darrow WT, Denmark SE. 2019. Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning. Science 363:eaau5631
    [Google Scholar]
  82. 82.
    Lipkowitz KB, D'Hue CA, Sakamoto T, Stack JN 2002. Stereocartography: a computational mapping technique that can locate regions of maximum stereoinduction around chiral catalysts. J. Am. Chem. Soc. 124:14255–67
    [Google Scholar]
  83. 83.
    Phan H, Hrudka JJ, Igimbayeva D, Lawson Daku LM, Shatruk M 2017. A simple approach for predicting the spin state of homoleptic Fe(II) tris-diimine complexes. J. Am. Chem. Soc. 139:6437–47
    [Google Scholar]
  84. 84.
    Holleis L, Shivaram BS, Balachandran PV. 2019. Machine learning guided design of single-molecule magnets for magnetocaloric applications. Appl. Phys. Lett. 114:222404
    [Google Scholar]
  85. 85.
    Taylor MG, Yang T, Lin S, Nandy A, Janet JP et al. 2020. Seeing is believing: experimental spin states from machine learning model structure predictions. J. Phys. Chem. A 124:3286–99
    [Google Scholar]
  86. 86.
    Chang AM, Freeze JG, Batista VS. 2019. Hammett neural networks: prediction of frontier orbital energies of tungsten–benzylidyne photoredox complexes. Chem. Sci. 10:6844–54
    [Google Scholar]
  87. 87.
    Yada A, Nagata K, Ando Y, Matsumura T, Ichinoseki S, Sato K. 2018. Machine learning approach for prediction of reaction yield with simulated catalyst parameters. Chem. Lett. 47:284–87
    [Google Scholar]
  88. 88.
    Mikami K. 2020. Interactive-quantum-chemical-descriptors enabling accurate prediction of an activation energy through machine learning. Polymer 203:122738
    [Google Scholar]
  89. 89.
    Yang WH, Fidelis TT, Sun WH. 2020. Prediction of catalytic activities of bis(imino)pyridine metal complexes by machine learning. J. Comput. Chem. 41:1064–67
    [Google Scholar]
  90. 90.
    Janet JP, Gani TZH, Steeves AH, Ioannidis EI, Kulik HJ. 2017. Leveraging cheminformatics strategies for inorganic discovery: application to redox potential design. Ind. Eng. Chem. Res. 56:4898–910
    [Google Scholar]
  91. 91.
    Henle JJ, Zahrt AF, Rose BT, Darrow WT, Wang Y, Denmark SE. 2020. Development of a computer-guided workflow for catalyst optimization. Descriptor validation, subset selection, and training set analysis. J. Am. Chem. Soc. 142:11578–92
    [Google Scholar]
  92. 92.
    Fanourgakis GS, Gkagkas K, Tylianakis E, Froudakis GE. 2020. A universal machine learning algorithm for large-scale screening of materials. J. Am. Chem. Soc. 142:3814–22
    [Google Scholar]
  93. 93.
    Xie T, Grossman JC. 2018. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120:145301
    [Google Scholar]
  94. 94.
    Moreau G, Broto P. 1980. The autocorrelation of a topological structure: a new molecular descriptor. Nouv. J. Chim. 4:359
    [Google Scholar]
  95. 95.
    Fernandez M, Trefiak NR, Woo TK. 2013. Atomic property weighted radial distribution functions descriptors of metal–organic frameworks for the prediction of gas uptake capacity. J. Phys. Chem. C 117:14095–105
    [Google Scholar]
  96. 96.
    Eckhoff M, Lausch KN, Blöchl PE, Behler J. 2020. Predicting oxidation and spin states by high-dimensional neural networks: applications to lithium manganese oxide spinels. J. Chem. Phys. 153:164107
    [Google Scholar]
  97. 97.
    Lunghi A, Sanvito S. 2020. Surfing multiple conformation-property landscapes via machine learning: designing single-ion magnetic anisotropy. J. Phys. Chem. C 124:5802–6
    [Google Scholar]
  98. 98.
    Pardakhti M, Moharreri E, Wanik D, Suib SL, Srivastava R. 2017. Machine learning using combined structural and chemical descriptors for prediction of methane adsorption performance of metal organic frameworks (MOFs). ACS Comb. Sci. 19:640–45
    [Google Scholar]
  99. 99.
    Duan C, Liu F, Nandy A, Kulik HJ. 2020. Data-driven approaches can overcome the cost-accuracy tradeoff in multireference diagnostics. J. Chem. Theory Comput. 16:4373–87
    [Google Scholar]
  100. 100.
    Rupp M, Tkatchenko A, Muller KR, von Lilienfeld OA 2012. Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108:058301
    [Google Scholar]
  101. 101.
    Borboudakis G, Stergiannakos T, Frysali M, Klontzas E, Tsamardinos I, Froudakis GE. 2017. Chemically intuited, large-scale screening of MOFs by machine learning techniques. npj Comput. Mater. 3:40
    [Google Scholar]
  102. 102.
    Anderson R, Rodgers J, Argueta E, Biong A, Gómez-Gualdrón DA. 2018. Role of pore chemistry and topology in the CO2 capture capabilities of MOFs: from molecular simulation to machine learning. Chem. Mater. 30:6325–37
    [Google Scholar]
  103. 103.
    Martin RL, Smit B, Haranczyk M. 2011. Addressing challenges of identifying geometrically diverse sets of crystalline porous materials. J. Chem. Inf. Model. 52:308–18
    [Google Scholar]
  104. 104.
    Willems TF, Rycroft CH, Kazi M, Meza JC, Haranczyk M. 2012. Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials. Microporous Mesoporous Mater 149:134–41
    [Google Scholar]
  105. 105.
    Duan C, Chen S, Taylor MG, Liu F, Kulik HJ. 2021. Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles. Chem. Sci. 12:13021–36
    [Google Scholar]
  106. 106.
    Liao P, Getman RB, Snurr RQ. 2017. Optimizing open iron sites in metal–organic frameworks for ethane oxidation: a first-principles study. ACS Appl. Mater. Interfaces 9:33484–92
    [Google Scholar]
  107. 107.
    Jia X, Lynch A, Huang Y, Danielson M, Lang'at I et al. 2019. Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis. Nature 573:251–55
    [Google Scholar]
  108. 108.
    Raccuglia P, Elbert KC, Adler PD, Falk C, Wenny MB et al. 2016. Machine-learning-assisted materials discovery using failed experiments. Nature 533:73–76
    [Google Scholar]
  109. 109.
    Umegaki T, Watanabe Y, Nukui N, Omata K, Yamada M. 2003. Optimization of catalyst for methanol synthesis by a combinatorial approach using a parallel activity test and genetic algorithm assisted by a neural network. Energy Fuels 17:850–56
    [Google Scholar]
  110. 110.
    Andersson M, Bligaard T, Kustov A, Larsen K, Greeley J et al. 2006. Toward computational screening in heterogeneous catalysis: Pareto optimal methanation catalysts. J. Catal. 239:501–6
    [Google Scholar]
  111. 111.
    Corma A, Serra J, Serna P, Valero S, Argente E, Botti V 2005. Optimisation of olefin epoxidation catalysts with the application of high-throughput and genetic algorithms assisted by artificial neural networks (soft computing techniques). J. Catal. 229:513–24
    [Google Scholar]
  112. 112.
    Cuéllar MP, Lapresta-Fernández A, Herrera JM, Salinas-Castillo A, Pegalajar MDC et al. 2015. Thermochromic sensor design based on Fe(II) spin crossover/polymers hybrid materials and artificial neural networks as a tool in modelling. Sens. Actuators B 208:180–87
    [Google Scholar]
  113. 113.
    Gustafson JA, Wilmer CE. 2019. Intelligent selection of metal–organic framework arrays for methane sensing via genetic algorithms. ACS Sens 4:1586–93
    [Google Scholar]
  114. 114.
    Tshitoyan V, Dagdelen J, Weston L, Dunn A, Rong Z et al. 2019. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571:95–98
    [Google Scholar]
  115. 115.
    Schweidtmann AM, Clayton AD, Holmes N, Bradford E, Bourne RA, Lapkin AA. 2018. Machine learning meets continuous flow chemistry: automated optimization towards the Pareto front of multiple objectives. Chem. Eng. J. 352:277–82
    [Google Scholar]
  116. 116.
    Batra R, Chen C, Evans TG, Walton KS, Ramprasad R. 2020. Prediction of water stability of metal–organic frameworks using machine learning. Nat. Mach. Intell. 2:704–10
    [Google Scholar]
  117. 117.
    Kreutz JE, Shukhaev A, Du W, Druskin S, Daugulis O, Ismagilov RF. 2010. Evolution of catalysts directed by genetic algorithms in a plug-based microfluidic device tested with oxidation of methane by oxygen. J. Am. Chem. Soc. 132:3128–32
    [Google Scholar]
  118. 118.
    Watanabe Y, Umegaki T, Hashimoto M, Omata K, Yamada M. 2004. Optimization of Cu oxide catalysts for methanol synthesis by combinatorial tools using 96 well microplates, artificial neural network and genetic algorithm. Catal. Today 89:455–64
    [Google Scholar]
  119. 119.
    Chung YG, Gómez-Gualdrón DA, Li P, Leperi KT, Deria P et al. 2016. In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm. Sci. Adv. 2:e1600909
    [Google Scholar]
  120. 120.
    Janet JP, Chan L, Kulik HJ 2018. Accelerating chemical discovery with machine learning: simulated evolution of spin crossover complexes with an artificial neural network. J. Phys. Chem. Lett. 9:1064–71
    [Google Scholar]
  121. 121.
    Jennings PC, Lysgaard S, Hummelshøj JS, Vegge T, Bligaard T. 2019. Genetic algorithms for computational materials discovery accelerated by machine learning. npj Comput. Mater. 5:46
    [Google Scholar]
  122. 122.
    Leardi R. 2001. Genetic algorithms in chemometrics and chemistry: a review. J. Chemom. 15:559–69
    [Google Scholar]
  123. 123.
    Le TC, Winkler DA. 2016. Discovery and optimization of materials using evolutionary approaches. Chem. Rev. 116:6107–32
    [Google Scholar]
  124. 124.
    Collins SP, Daff TD, Piotrkowski SS, Woo TK. 2016. Materials design by evolutionary optimization of functional groups in metal-organic frameworks. Sci. Adv. 2:e1600954
    [Google Scholar]
  125. 125.
    Lee S, Kim B, Cho H, Lee H, Lee SY et al. 2021. Computational screening of trillions of metal–organic frameworks for high-performance methane storage. ACS Appl. Mater. Interfaces 13:23647–54
    [Google Scholar]
  126. 126.
    Foscato M, Venkatraman V, Jensen VR. 2019. DENOPTIM: software for computational de novo design of organic and inorganic molecules. J. Chem. Inf. Model. 59:4077–82
    [Google Scholar]
  127. 127.
    Llamas-Galilea J, Gobin OC, Schüth F. 2009. Comparison of single- and multiobjective design of experiment in combinatorial chemistry for the selective dehydrogenation of propane. J. Comb. Chem. 11:907–13
    [Google Scholar]
  128. 128.
    Martin RL, Haranczyk M. 2013. Insights into multi-objective design of metal–organic frameworks. Cryst. Growth Des. 13:4208–12
    [Google Scholar]
  129. 129.
    Gopakumar AM, Balachandran PV, Xue D, Gubernatis JE, Lookman T. 2018. Multi-objective optimization for materials discovery via adaptive design. Sci. Rep. 8:3738
    [Google Scholar]
  130. 130.
    Yuan R, Liu Z, Balachandran PV, Xue D, Zhou Y et al. 2018. Accelerated discovery of large electrostrains in BaTiO3-based piezoelectrics using active learning. Adv. Mater. 30:1702884
    [Google Scholar]
  131. 131.
    del Rosario Z, Rupp M, Kim Y, Antono E, Ling J 2020. Assessing the frontier: active learning, model accuracy, and multi-objective candidate discovery and optimization. J. Chem. Phys. 153:024112
    [Google Scholar]
  132. 132.
    Zhang Y, Lee AA. 2019. Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning. Chem. Sci. 10:8154–63
    [Google Scholar]
  133. 133.
    Forrester AIJ, Keane AJ. 2009. Recent advances in surrogate-based optimization. Prog. Aeronaut. Sci. 45:50–79
    [Google Scholar]
  134. 134.
    Rodemerck U, Baerns M, Holena M, Wolf D. 2004. Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials. Appl. Surf. Sci. 223:168–74
    [Google Scholar]
  135. 135.
    Huang K, Chen F-Q, D-W. 2001. Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling. Appl. Catal. A 219:61–68
    [Google Scholar]
  136. 136.
    Huang K, Zhan X-L, Chen F-Q, D-W. 2003. Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm. Chem. Eng. Sci. 58:81–87
    [Google Scholar]
  137. 137.
    Chu Y, Heyndrickx W, Occhipinti G, Jensen VR, Alsberg BK. 2012. An evolutionary algorithm for de novo optimization of functional transition metal compounds. J. Am. Chem. Soc. 134:8885–95
    [Google Scholar]
  138. 138.
    Rizkin BA, Hartman RL. 2019. Supervised machine learning for prediction of zirconocene-catalyzed α-olefin polymerization. Chem. Eng. Sci. 210:115224
    [Google Scholar]
  139. 139.
    Amar Y, Schweidtmann A, Deutsch P, Cao LW, Lapkin A. 2019. Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis. Chem. Sci. 10:6697–706
    [Google Scholar]
  140. 140.
    Siebert M, Krennrich G, Seibicke M, Siegle AF, Trapp O. 2019. Identifying high-performance catalytic conditions for carbon dioxide reduction to dimethoxymethane by multivariate modelling. Chem. Sci. 10:10466–74
    [Google Scholar]
  141. 141.
    Chen H-Z, Zhang Y-Y, Gong X, Xiang H. 2014. Predicting new TiO2 phases with low band gaps by a multiobjective global optimization approach. J. Phys. Chem. C 118:2333–37
    [Google Scholar]
  142. 142.
    Herbol HC, Hu W, Frazier P, Clancy P, Poloczek M 2018. Efficient search of compositional space for hybrid organic–inorganic perovskites via Bayesian optimization. npj Comput. Mater. 4:51
    [Google Scholar]
  143. 143.
    Park S, Kim B, Choi S, Boyd PG, Smit B, Kim J. 2018. Text mining metal–organic framework papers. J. Chem. Inf. Model. 58:244–51
    [Google Scholar]
  144. 144.
    Scott DJ, Manos S, Coveney PV. 2008. Design of electroceramic materials using artificial neural networks and multiobjective evolutionary algorithms. J. Chem. Inf. Model. 48:262–73
    [Google Scholar]
  145. 145.
    Cáceres EL, Mew NC, Keiser MJ. 2020. Adding stochastic negative examples into machine learning improves molecular bioactivity prediction. J. Chem. Inf. Model. 60:5957–70
    [Google Scholar]
  146. 146.
    Kim B, Lee S, Kim J. 2020. Inverse design of porous materials using artificial neural networks. Sci. Adv. 6:eaax9324
    [Google Scholar]
  147. 147.
    Yao Z, Sánchez-Lengeling B, Bobbitt NS, Bucior BJ, Kumar SGH et al. 2021. Inverse design of nanoporous crystalline reticular materials with deep generative models. Nat. Mach. Intell. 3:76–86
    [Google Scholar]
  148. 148.
    Dan Y, Zhao Y, Li X, Li S, Hu M, Hu J. 2020. Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. npj Comput. Mater. 6:84
    [Google Scholar]
  149. 149.
    Jensen Z, Kwon S, Schwalbe-Koda D, Paris C, Gómez-Bombarelli R et al. 2021. Discovering relationships between OSDAs and zeolites through data mining and generative neural networks. ACS Cent. Sci. 7:858–67
    [Google Scholar]
  150. 150.
    Ramakrishnan R, Dral PO, Rupp M, von Lilienfeld OA 2015. Big data meets quantum chemistry approximations: the Δ-machine learning approach. J. Chem. Theory Comput. 11:2087–96
    [Google Scholar]
/content/journals/10.1146/annurev-chembioeng-092320-120230
Loading
/content/journals/10.1146/annurev-chembioeng-092320-120230
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error