Artificial Intelligence in Material Genomics
Joy Hoskeri H, Nivedita Pujari S, Badrinath Kulkarni, Arun K. Shettar
The invention of new materials with desired properties is always a matter of interest. The material genome projects and material genome initiative have bought new insights into material genomics. Artificial intelligence (AI) is the decision-making ability of a computable machine. AI in material genomics has stimulated the field of material science. AI tools like Atom2vec, MATLAB, ICSD, MPIinterfaces, PyCDT, and AFLOWLIB have opened ways for the discovery of various materials. These AI tools and databases are also efficient in the property prediction of new materials, improvement in characterization protocols, experimental parameter standardization, fastening simulation scale, development of high throughput methods, and data analysis. The current chapter is focused on AI-based developments in the material genomics.
Keywords
Material Genomics, Artificial Intelligence, Machine Learning, AI Tools, and Databases
Published online , 18 pages
Citation: Joy Hoskeri H, Nivedita Pujari S, Badrinath Kulkarni, Arun K. Shettar, Artificial Intelligence in Material Genomics, Materials Research Foundations, Vol. 147, pp 87-104, 2023
DOI: https://doi.org/10.21741/9781644902530-4
Part of the book on Application of Artificial Intelligence in New Materials Discovery
References
[1] K. Yang, W. Setyawan, S. Wang, M.B. Nardelli, S. Curtarolo, A search model for topological insulators with high-throughput robustness descriptors, Nat. Mater. 11 (2012) 614-619. https://doi.org/10.1038/nmat3332
[2] De Pablo, J. Juan, B. Jones, C.L. Kovacs, V. Ozolins, A.P. Ramirez, The materials genome initiative, the interplay of experiment, theory and computation, Curr. Opin. Solid State Mater. Sci. 18 (2014) 99-117. https://doi.org/10.1016/j.cossms.2014.02.003
[3] G. Hautier, A. Jain, S.P. Ong, From the computer to the laboratory: Materials discovery and design using first principles calculations, J. Mater. Sci. 47 (2012) 7317-7340. https://doi.org/10.1007/s10853-012-6424-0
[4] A.M. Turing, Computing machinery and intelligence, in: R. Epstein, G. Roberts, G. Beber (Eds.), Parsing the Turing Test, Springer, Dordrecht, 2009, pp. 23-65. https://doi.org/10.1007/978-1-4020-6710-5_3
[5] Hanson III, C. William, B.E. Marshall, Artificial intelligence applications in the intensive care unit, Crit. Care Med. 29 (2001) 427-435. https://doi.org/10.1097/00003246-200102000-00038
[6] J. Bullock, A. Luccioni, K.H. Pham, C.S.N. Lam, M.L. Oroz, Mapping the landscape of artificial intelligence applications against COVID-19, J. Artif. Intell. Res. 69 (2020) 807-845. https://doi.org/10.1613/jair.1.12162
[7] J. He, J. Chen, X. He, J. Gao, L. Li, L. Deng, M. Ostendorf, Deep reinforcement learning with a natural language action\ space, arXiv preprint arXiv 1511.04636 (2015). https://doi.org/10.18653/v1/P16-1153
[8] S. Ye, B. Li, Q. Li, H.-P. Zhao, X.-Q. Feng, Deep neural network method for predicting the mechanical properties of composites, Appl. Phys. Lett. 115 (2019) 161901. https://doi.org/10.1063/1.5124529
[9] Y. Zhuo, A.M. Tehrani, A.O. Oliynyk, A.C. Duke, J. Brgoch, Identifying an efficient, thermally robust inorganic Phosphor host via machine learning, Nat. Commun. 9 (2018) 1-10. https://doi.org/10.1038/s41467-017-02088-w
[10] Wang, Y.Tang, B. Li, X.-J. Xu, H.-B. Ren, J.-Y. Yin, H. Zhu, Y.H. Zhang, FTIR spectroscopy coupled with machine learning approaches as a rapid tool for identification and quantification of artificial sweeteners, Food Chem. 303 (2020) 125404. https://doi.org/10.1016/j.foodchem.2019.125404
[11] K.A. Severson, P.M. Attia, N. Jin, N. Perkins, B. Jiang, Z. Yang, M.H. Chen, M. Aykol, P.K. Herring, D. Fraggedakis, M.Z. Bazant, S.J. Harris, W.C. Chueh, R.D. Braatz, Data driven prediction of battery cycle life before capacity degradation, Nat. Energy. 4 (2019) 383-391. https://doi.org/10.1038/s41560-019-0356-8
[12] A. Dima, S. Bhaskarla, C. Becker, M. Brady, C. Campbell, P. Dessauw, R. Hanisch, U. Kattner, K. Kroenlein, M. Newrock, A. Peskin, R. Plante, S.Y. Li, P.F. Rigodiat, G.S. Amaral, Z. Trautt, X. Schmitt, J. Warren, S. Youssef, Informatics infrastructure for the materials genome initiative, Jom. 68 (2016) 2053-2064. https://doi.org/10.1007/s11837-016-2000-4
[13] Lu, X.-Gang, Remarks on the recent progress of Materials Genome Initiative, Sci. Bull. 60 (2015) 1966 1968. https://doi.org/10.1007/s11434-015-0937-2
[14] G. Khaira, M. Doxastakis, A. Bowen, J. Ren, H.S. Suh, T.S. Peretz, X.Chen, C. Zhou, A.F. Hannon, N.J. Ferrier, V. Vishwanath, D.F. Sunday, R. Gronheid, R.J. Kline, J.J. De Pablo, P.F. Nealey, Derivation of multiple covarying material and process parameters using physics-based modeling of X-ray data, Macromolecules. 50 (2017) 7783-7793. https://doi.org/10.1021/acs.macromol.7b00691
[15] G. Bombarelli, Rafael, J.A.-Iparraguirre, T.D. Hirzel, D.Duvenaud, D.Maclaurin, M.A. B.-Forsythe, H. S. Chae, M. Einzinger, D.G. Ha, T. Wu, G. Marcopoulos, S. Jeon, H. Kang, H. Miyazaki, M. Numata, S. Kim, W. Huang, S.I. Hong, M. Baldo, R.P. Adams, A.A. Guzik, Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach, Nat. Mater. 15 (2016) 1120-1127. https://doi.org/10.1038/nmat4717
[16] T.H. Kim, D. Puggioni, Y. Yuan, L. Xie, H. Zhou, N. Campbell, P.J. Ryan, Y. Choi, J.W. Kim, J.R. Patzner, S. Ryu, J.R. Podkaminer, J. Irwin, Y. Ma, C.J. Fennie, M.S. Rzchowski, X.Q. Pan, V. Gopalan, J.M. Rondinelli, C.B. Eom, Polar metals by geometric design, Nature. 533 (2016) 68-72. https://doi.org/10.1038/nature17628
[17] Y. Wang, Y. Liu, S. Song, Z. Yang, X. Qi, K. Wang, Y. Liu, Q. Zhang, Y. Tian, Accelerating the discovery of insensitive high-energy-density materials by a materials genome approach, Nat. Commun. 9 (2018) 1-11. https://doi.org/10.1038/s41467-017-02088-w
[18] L. Yu, A. Zunger, Identification of potential photovoltaic absorbers based on first-principles spectroscopic screening of materials, Phys. Rev. Lett. 108 (2012) 068701. https://doi.org/10.1103/PhysRevLett.108.068701
[19] J. Hachmann, R.O. Amaya, S.A. Evrenk, C.A. Bedolla, R.S.S. Carrera, A.G. Parker, L.Vogt, A.M. Brockway, A.A. Guzik, The Harvard clean energy project: Large-scale computational screening and design of organic photovoltaics on the world community grid, J. Phys. Chem. Lett. 2 (2011) 2241-2251. https://doi.org/10.1021/jz200866s
[20] A. Jain, S.P. Ong, G. Hautier, W.Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson, Commentary: The Materials Project: A materials genome approach to accelerating materials innovation, APL Mater. 1 (2013) 011002. https://doi.org/10.1063/1.4812323
[21] Y.T. Liang, B.K. Vijayan, K.A. Gray, M.C. Hersam, Minimizing graphene defects enhances titania nanocomposite based photocatalytic reduction of CO2 for improved solar fuel production, Nano Lett. 11 (2011) 2865-2870. https://doi.org/10.1021/nl2012906
[22] H. Zhao, X. Li, Y. Zhang, L.S. Schadler, W. Chen, L.C. Brinson, Perspective: NanoMine: A material genome approach for polymer nanocomposites analysis and design, APL Mater. 4 (2016) 053204. https://doi.org/10.1063/1.4943679
[23] Y. Liu, C. Niu, Z. Wang, Y. Gan, Y. Zhu, S. Sun, T. Shen, Machine learning in materials genome initiative: A review, J. Mater. Sci. Technol. 57 (2020) 113-122. https://doi.org/10.1016/j.jmst.2020.01.067
[24] G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Sci. 313 (2006) 504-507. https://doi.org/10.1126/science.1127647
[25] O.Z. Richter, V.I. Marín, M. Bond, F. Gouverneur, Systematic review of research on artificial intelligence applications in higher education-where are the educators?, Int. J. Educ. Technol. High. Educ. 16 (2019) 1-27. https://doi.org/10.1186/s41239-019-0132-7
[26] J. Chen, D. Remulla, J.H. Nguyen, Y. Liu, P. Dasgupta, A.J. Hung, Current status of artificial intelligence applications in urology and their potential to influence clinical practice, BJU Int. 124 (2019) 567-577. https://doi.org/10.1111/bju.14852
[27] M. Abdallah, M.A. Talib, S. Feroz, Q. Nasir, H. Abdalla, B. Mahfood, Artificial intelligence applications in solid waste management: A systematic research review, J. Waste Manag. 109 (2020) 231-246. https://doi.org/10.1016/j.wasman.2020.04.057
[28] J. Xu, P. Yang, S. Xue, B. Sharma, M.S. Martin, F. Wang, K.A. Beaty, E. Dehan, B. Parikh, Translating cancer genomics into precision medicine with artificial intelligence: Applications, challenges and future perspectives, Hum. Genet. 138 (2019) 109-124. https://doi.org/10.1007/s00439-019-01970-5
[29] F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, Y. Wang, Artificial intelligence in healthcare: Past, present and future, Stroke Vasc. Neurol. 2 (2017) 000101. https://doi.org/10.1136/svn-2017-000101
[30] E. Chukwu, N. Clara, Applications of artificial intelligence in agriculture: A review, Eng. Technol. Appl. Sci. Res. 9 (2019) 4377-4383. https://doi.org/10.48084/etasr.2756
[31] R. Kline, Cybernetics, automata studies and the Dartmouth conference on artificial intelligence, IEEE Ann. Hist. Comput. 33 (2010) 5-16. https://doi.org/10.1109/MAHC.2010.44
[32] McCulloch, S. Warren, W. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys. 5 (1943) 115-133. https://doi.org/10.1007/BF02478259
[33] D.T. Tran, S. Kiranyaz, M. Gabbouj, A. Iosifidis, Heterogeneous multilayer generalized operational perceptron, IEEE Trans. Neural Netw. Learn. Syst. 31 (2019) 710-724. https://doi.org/10.1109/TNNLS.2019.2914082
[34] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G.V.D. Driessche, J. Schrittwieser, et al., Mastering the game of Go with deep neural networks and tree search, Nature. 529 (2016) 484-489. https://doi.org/10.1038/nature16961
[35] J.J. Möller, W. Körner, G. Krugel, D.F. Urban, C. Elsässer, Compositional optimization of hard magnetic phases with machine-learning models, Acta Mater. 153 (2018) 53-61. https://doi.org/10.1016/j.actamat.2018.03.051
[36] M. Schmidt, H. Lipson, Distilling free-form natural laws from experimental data, Science. 324 (2009) 81-85. https://doi.org/10.1126/science.1165893
[37] H. Salmenjoki, M.J. Alava, L. Laurson, Machine learning\ plastic deformation of crystals, Nat. Commun. 9 (2018) 1 7. https://doi.org/10.1038/s41467-018-07737-2
[38] de Almeida, A. Filipa, R. Moreira, T. Rodrigues, Synthetic organic chemistry driven by artificial intelligence, Nat. Rev. Chem. 3 (2019) 589-604. https://doi.org/10.1038/s41570-019-0124-0
[39] N. Schneider, D.M. Lowe, R.A. Sayle, G.A. Landrum, Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity, J. Chem. Inf. Model. 55 (2015) 39-53. https://doi.org/10.1021/ci5006614
[40] A.D. Sendek, E.D. Cubuk, E.R. Antoniuk, G.Cheon, Y.Cui, E.J. Reed, Machine learning-assisted discovery of solid Li ion conducting materials, Chem. Mater. 31 (2018) 342-352. https://doi.org/10.1021/acs.chemmater.8b03272
[41] E. Kim, K. Huang, A. Saunders, A. McCallum, G. Ceder, E. Olivetti, Materials synthesis insights from scientific literature via text extraction and machine learning, Chem.Mater. 29 (2017) 9436-9444. https://doi.org/10.1021/acs.chemmater.7b03500
[42] M. Sun, T. Wu, Y. Xue, A.W. Dougherty, B. Huang, Y. Li, C.H. Yan, Mapping of atomic catalyst on graphdiyne, Nano Energy 62 (2019) 754-763. https://doi.org/10.1016/j.nanoen.2019.06.008
[43] S. Lu, Q. Zhou, Y. Ouyang, Y. Guo, Q. Li, J. Wang, Accelerated discovery of stable Lead-free hybrid organic inorganic perovskites via machine learning, Nat. Commun. 9 (2018) 1-8. https://doi.org/10.1038/s41467-017-02088-w
[44] P.M. Attia, A. Grover, N. Jin, K.A. Severson, T.M. Markov, Y.H. Liao, M.H. Chen, B. Cheong, N. Perkins, Z. Yang, P.K. Herring, M. Aykol, S.J. Harris, R.D. Braatz, S. Ermon, W.C. Chueh, Closed loop optimization of fast-charging protocols for batteries with machine learning, Nature 578 (2020) 397-402. https://doi.org/10.1038/s41586-020-1994-5
[45] Y. Du, T. Mukherjee, T. DebRoy, Conditions for void formation in friction stir welding from machine learning, NPJ Comput. Mater. 5 (2019) 1-8. https://doi.org/10.1038/s41524-019-0207-y
[46] A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst. 25 (2012) 84-90. https://doi.org/10.1145/3065386
[47] B. Lin, J.L. Hedrick, N.H. Park, R.M. Waymouth, Programmable high-throughput platform for the rapid and scalable synthesis of polyester and polycarbonate libraries, J. Am. Chem. Soc. 141 (2019) 8921-8927. https://doi.org/10.1021/jacs.9b02450
[48] B. Lin, J.L. Hedrick, N.H. Park, R.M. Waymouth, Programmable high-throughput platform for the rapid and scalable synthesis of polyester and polycarbonate libraries, J. Am. Chem. Soc. 141 (2019) 8921-8927. https://doi.org/10.1021/jacs.9b02450
[49] M. Ziatdinov, A. Maksov, S.V. Kalinin, Learning surface molecular structures via machine vision, NPJ Comput. Mater. 3 (2017) 1-9. https://doi.org/10.1038/s41524-017-0038-7
[50] A. Belsky, M. Hellenbrandt, V.L. Karen, P. Luksch, New developments in the Inorganic Crystal Structure Database (ICSD): Accessibility in support of materials research and design, Acta Crystallogr: B, Struct. Sci. 58 (2002) 364-369. https://doi.org/10.1107/S0108768102006948
[51] J.E. Saal, S. Kirklin, M. Aykol, B. Meredig, C. Wolverton, Materials design and discovery with high throughput density functional theory: The open quantum materials database (OQMD), Jom. 65 (2013) 1501-1509. https://doi.org/10.1007/s11837-013-0755-4
[52] N. Mounet, M. Gibertini, P. Schwaller, D. Campi, A. Merkys, A. Marrazzo, T. Sohier, I.E Castelli, A. Cepellotti, G. Pizzi, N. Marzari, Two dimensional materials from high-throughput computational exfoliation of experimentally known compounds, Nat. Nanotechnol. 13 (2018) 246-252. https://doi.org/10.1038/s41565-017-0035-5
[53] I. Takeuchi, R.B. Dover, H. Koinuma, Combinatorial synthesis and evaluation of functional inorganic materials using thin film techniques, MRS Bull. 27 (2002) 301-308. https://doi.org/10.1557/mrs2002.97
[54] S. Curtarolo, W. Setyawan, G.L.W. Hart, M. Jahnatek, R.V. Chepulskii, R.H. Taylor, S. Wang, J. Xue, K. Yang, O. Levy, M.J. Mehl, H.T. Stokes, D.O. Demchenko, D. Morgan, AFLOW: An automatic framework for high-throughput materials discovery, Comput. Mater. Sci. 58 (2012) 218-226. https://doi.org/10.1016/j.commatsci.2012.02.005
[55] M. Morales, J. Paul, A. Fumarola, V. Taliaronak, A. Shirsekar, J. Kidner, Z. Ali, M. Ali, Electronic band structure screening for Dirac points in Heuslers, arXiv preprint arXiv:2205.02547 (2022).
