Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-30T07:32:44.912Z Has data issue: false hasContentIssue false
  • English
  • Français

Growing field of materials informatics: databases and artificial intelligence

Published online by Cambridge University Press:  14 January 2020

Alejandro Lopez-Bezanilla Open the ORCID record for Alejandro Lopez-Bezanilla [Opens in a new window]  and
Peter B. Littlewood Open the ORCID record for Peter B. Littlewood [Opens in a new window]
Alejandro Lopez-Bezanilla*
Affiliation:
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM87545, USA
Peter B. Littlewood
Affiliation:
Argonne National Laboratory, Lemont, IL60439, USA James Franck Institute, University of Chicago, Chicago, IL60637, USA
*
Address all correspondence to Alejandro Lopez-Bezanilla at [email protected]
Get access

Abstract

The paradigm of molecular discovery in the chemical and pharmaceutical industry has followed a repetitive succession of screening and synthesis, involving the analysis of individual molecules that were both natural and produced. This ability to generate and screen libraries of compounds has found an echo in solid-state physics with the demand to explore and produce new materials for testing. In response to this demand, a golden age of materials discovery is being developed, with progress on important areas of both basic science and device applications. The confluence of theoretical and simulation methods, together with the availability of computation resources, has established the “materials genome” approach that is used by a growing number of research groups around the world with the goal of innovating on materials through systematic discovery. In this Prospective, an overview of this group of methodologies in tackling the ever-increasing complexity of computational materials science simulations is provided. Computational simulation is highlighted as a major component of rational design and synthesis of new materials with targeted properties, describing progress on databases and large data treatment. Tools for new materials discovery, including progress on the deployment of new data repositories, the implementation of high-throughput simulation approaches, and the development of artificial intelligence algorithms, are discussed.

Type
Artificial Intelligence Prospective Article
Information
MRS Communications , Volume 10 , Issue 1 , March 2020 , pp. 1 - 10
Check for updates
Copyright
Copyright © Materials Research Society 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.CERN Data Centre passes the 200-petabyte milestone. https://home.cern/news/news/computing/cern-data-centre-passes-200-petabyte-milestoneGoogle Scholar
2.Xue, D., Balachandran, P.V., Hogden, J., Theiler, J., Xue, D., and Lookman, T.: Accelerated search for materials with targeted properties by adaptive design. Nat. Commun. 7, 11241 (2016).CrossRefGoogle ScholarPubMed
3.Xue, D., Balachandran, P.V., Yuan, R., Hu, T., Qian, X., Dougherty, E.R., and Lookman, T.: Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning. Proc. Natl. Acad. Sci. 113, 13301 (2016).CrossRefGoogle ScholarPubMed
4.Belsky, A., Hellenbrandt, M., Karen, V.L., and Luksch, P.: New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design. Acta Crystallogr. B 58, 364 (2002).CrossRefGoogle ScholarPubMed
5.Grazulis, S., Chateigner, D., Downs, R.T., Yokochi, A.F.T., Quiros, M., Lutterotti, L., Manakova, E., Butkus, J., Moeck, P., and Le Bail, A.: Crystallography Open Database - an open-access collection of crystal structures. J. Appl. Crystallogr. 42, 726 (2009).CrossRefGoogle ScholarPubMed
6.Le Bail, A.: Inorganic structure prediction with GRINSP. J. Appl. Crystallogr. 38, 389 (2005).CrossRefGoogle Scholar
7.Materials Genome Initiative for Global Competitiveness, white paper, Group on Advanced Materials, June 2011. www.mgi.govGoogle Scholar
8.Fayyad, U., PiatetskyShapiro, G., and Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37 (1996).Google Scholar
9.Kuhn, K.J. et al. : The ultimate CMOS device and beyond. In Electron Devices Meeting (IEDM), 2012 IEEE International (IEEE, 2012). http://doi.org/10.1109/IEDM.2012.6479001.CrossRefGoogle Scholar
10.Crabtree, G., Kocs, E., and Trahey, L.: The energy-storage frontier: Lithium-ion batteries and beyond. MRS Bull. 40, 1067–1078 (2015).CrossRefGoogle Scholar
11.Aroyo, M., Perez-Mato, J., Orobengoa, D., Tasci, E., De La Flor, G., and Kirov, A.: Crystallography online: Bilbao crystallographic server. Chem. Commun. 43, 183 (2011), cited By 165.Google Scholar
12.Saal, J.E., Kirklin, S., Aykol, M., Meredig, B., and Wolverton, C.: Materials design and discovery with high-throughput density functional theory: The open quantum materials database (OQMD). JOM 65, 1501 (2013).CrossRefGoogle Scholar
13.Kirklin, S., Saal, J.E., Meredig, B., Thompson, A., Doak, J.W., Aykol, M., Ruehl, S., and Wolverton, C.: The open quantum materials database (OQMD): assessing the accuracy of DFT formation energies. NPJ Comput. Mater. 1, 1501 (2015). http://doi.org/10.1038/npjcompumats.2015.10.CrossRefGoogle Scholar
14.Villars, P., Onodera, N., and Iwata, S.: The Linus Pauling file (LPF) and its application to materials design. J. Alloys. Compd. 279, 1 (1998).CrossRefGoogle Scholar
15.https://materialsweb.org/Google Scholar
16.van de Walle, A., Nataraj, C., and Liu, Z.-K.: The thermodynamic database. Calphad 61, 173 (2018).CrossRefGoogle Scholar
17.SuperCon: http://supercon.nims.go.jpGoogle Scholar
18.Sumpter, B.G., Vasudevan, R.K., Potok, T., and Kalinin, S.V.: A bridge for accelerating materials by design. NPJ Comput. Mater. 1 (2015). http://doi.org/10.1038/npjcompumats.2015.8CrossRefGoogle Scholar
19.Jain, A., Ong, S.P., Hautier, G., Chen, W., Richards, W.D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., and Persson, K.A.: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).CrossRefGoogle Scholar
20.Legrain, F., Carrete, J., van Roekeghem, A., Curtarolo, S., and Mingo, N.: How chemical composition alone can predict vibrational free energies and entropies of solids. Chem. Mater. 29, 6220 (2017). http://doi.org/10.1021/acs.chemmater.7b00789.CrossRefGoogle Scholar
21.Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I.E., Cepellotti, A., Pizzi, G., and Marzari, N.: Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nat. Nanotechnol. 13, 246 (2018).CrossRefGoogle ScholarPubMed
22.Court, C.J. and Cole, J.M.: Auto-generated aterials database of Curie and Neél temperatures via semisupervised relationship extraction. Sci. Data 5, 180111 (2018). http://doi.org/10.1038/sdata.2018.111CrossRefGoogle Scholar
23.Rasmussen, F.A. and Thygesen, K.S.: Computational 2D materials database: Electronic structure of transition-metal dichalcogenides and oxides. J. Phys. Chem. C 119, 13169 (2015). http://doi.org/10.1021/acs.jpcc.5b02950.CrossRefGoogle Scholar
24.Özçelik, V.O., Azadani, J.G., Yang, C., Koester, S.J., and Low, T.: Band alignment of two-dimensional semiconductors for designing heterostructures with momentum space matching. Phys. Rev. B 94, 035125 (2016).CrossRefGoogle Scholar
25.Computational Materials Repository. https://cmr.fysik.dtu.dkGoogle Scholar
26.Haastrup, S., Strange, M., Pandey, M., Deilmann, T., Schmidt, P.S., Hinsche, N.F., Gjerding, M.N., Torelli, D., Larsen, P.M., Riis-Jensen, A.C., Gath, J., Jacobsen, K.W., Mortensen, J.J., Olsen, T., and Thygesen, K.S.: The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals. 2D Mater. 5, 042002 (2018).CrossRefGoogle Scholar
27.Ramakrishnan, R., Dral, P.O., Rupp, M., and von Lilienfeld, O.A.: Quantum chemistry structures and properties of 134 kilo molecules.Sci. Data 1, 140022 (2014) http://doi.org/10.1038/sdata.2014.22CrossRefGoogle ScholarPubMed
28.Huan, T.D., Mannodi-Kanakkithodi, A., Kim, C., Sharma, V., Pilania, G., and Ramprasad, R.: A polymer dataset for accelerated property prediction and design. Sci. Data 3, 160012 (2016) http://doi.org/10.1038/sdata.2016.12CrossRefGoogle ScholarPubMed
29.Petousis, I., Mrdjenovich, D., Ballouz, E., Liu, M., Winston, D., Chen, W., Graf, T., Schladt, T.D., Persson, K.A., and Prinz, F.B.: High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials. Sci. Data 4, 160134 (2017). http://doi.org/10.1038/sdata.2016.134CrossRefGoogle ScholarPubMed
30.de Jong, M., Chen, W., Angsten, T., Jain, A., Notestine, R., Gamst, A., Sluiter, M., Ande, C.K., van der Zwaag, S., Plata, J.J., Toher, C., Curtarolo, S., Ceder, G., Persson, K.A., and Asta, M.: Charting the complete elastic properties of inorganic crystalline compounds. Sci. Data 2, 150009 (2015). http://doi.org/10.1038/sdata.2015.9CrossRefGoogle ScholarPubMed
31.Draxl, C. and Scheffler, M.: NOMAD: The FAIR concept for big data-driven materials science. MRS Bull. 43, 676–682 (2018).CrossRefGoogle Scholar
32.MaterialsCommons: https://materialscommons.orgGoogle Scholar
33.4CeeD: https://4ceed.github.io/Google Scholar
34.The Materials Data Facility (MDF): https://materialsdatafacility.org/Google Scholar
35.NIST: NIST Materials Resource Registry. https://www.nist.gov/programs-projects/nist-materials-resource-registryGoogle Scholar
36.The AI platform for materials development. https://citrine.io/Google Scholar
37.NIST: NIST Materials Data Curation System. https://www.nist.gov/programs-projects/materials-data-curation-systemGoogle Scholar
38.Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R.H., Nelson, L.J., Hart, G.L., Sanvito, S., Buongiorno-Nardelli, M., Mingo, N., and Levy, O.: AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations. Comput. Mater. Sci. 58, 227 (2012).CrossRefGoogle Scholar
39.Jain, A., Ong, S.P., Chen, W., Medasani, B., Qu, X., Kocher, M., Brafman, M., Petretto, G., Rignanese, G.-M., Hautier, G., Gunter, D., and Persson, K.A.: FireWorks: a dynamic workflow system designed for high-throughput applications. Concurr. Comput. Pract. Exper. 27, 5037 (2015), cPE-14-0307.R2.CrossRefGoogle Scholar
40.Takeuchi, I., Dover, R.B.V., and Koinuma, H.: Combinatorial synthesis and evaluation of functional inorganic materials using thin-film techniques. MRS Bull. 27, 301–308 (2002).CrossRefGoogle Scholar
41.Curtarolo, S., Setyawan, W., Hart, G.L., Jahnatek, M., Chepulskii, R.V., Taylor, R.H., Wang, S., Xue, J., Yang, K., Levy, O., Mehl, M.J., Stokes, H.T., Demchenko, D.O., and Morgan, D.: AFLOW: An automatic framework for high-throughput materials discovery. Comput. Mater. Sci. 58, 218 (2012).CrossRefGoogle Scholar
42.Setyawan, W., Gaume, R.M., Lam, S., Feigelson, R.S., and Curtarolo, S.: High-throughput combinatorial database of electronic band structures for inorganic scintillator materials. ACS Comb. Sci. 13, 382 (2011). http://doi.org/10.1021/co200012wCrossRefGoogle ScholarPubMed
43.Kuhar, K., Pandey, M., Thygesen, K.S., and Jacobsen, K.W.: High-throughput computational assessment of previously synthesized semiconductors for photovoltaic and photoelectrochemical devices. ACS Energy Lett. 3, 436 (2018). http://doi.org/10.1021/acsenergylett.7b01312CrossRefGoogle Scholar
44.Varley, J.B., Miglio, A., Ha, V.-A., van Setten, M.J., Rignanese, G.-M., and Hautier, G.: High-throughput design of non-oxide p-type transparent conducting materials: Data mining, search strategy, and identification of boron phosphide. Chem. Mater. 29, 2568 (2017). http://doi.org/10.1021/acs.chemmater.6b04663CrossRefGoogle Scholar
45.Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N., and Kozinsky, B.: AiiDA: automated interactive infrastructure and database for computational science. Comput. Mater. Sci. 111, 218 (2016).CrossRefGoogle Scholar
46.Singh, A., Mathew, K., Davydov, A.V., Hennig, R.G., and Tavazza, F.: High throughput screening of substrates for synthesis and functionalization of 2D materials (2015) https://www.nist.gov/publications/high-throughput-screening-substrates-synthesis-and-functionalization-2d-materials.CrossRefGoogle Scholar
47.Yuan, R., Liu, Z., Balachandran, P.V., Xue, D., Zhou, Y., Ding, X., Sun, J., Xue, D., and Lookman, T.: Accelerated discovery of large electrostrains in BaTiO3-based piezoelectrics using active learning. Adv. Mater. 30, 1702884 (2018).CrossRefGoogle ScholarPubMed
48.Cooper, C.B., Beard, E.J., Vazquez-Mayagoitia, l., Stan, L., Stenning, G.B.G., Nye, D.W., Vigil, J.A., Tomar, T., Jia, J., Bodedla, G.B., Chen, S., Gallego, L., Franco, S., Carella, A., Thomas, K.R.J., Xue, S., Zhu, X., and Cole, J.M.: Design-to-device approach affords panchromatic co-sensitized solar cells. Adv. Energy Mater. 9, 1802820 (2019).CrossRefGoogle Scholar
49.Mathew, K., Singh, A.K., Gabriel, J.J., Choudhary, K., Sinnott, S.B., Davydov, A.V., Tavazza, F., and Hennig, R.G.: MPInterfaces: A Materials Project based Python tool for high-throughput computational screening of interfacial systems. Comput. Mater. Sci. 122, 183 (2016).CrossRefGoogle Scholar
50.Ward, L., Dunn, A., Faghaninia, A., Zimmermann, N.E., Bajaj, S., Wang, Q., Montoya, J., Chen, J., Bystrom, K., Dylla, M., Chard, K., Asta, M., Persson, K.A., Snyder, G.J., Foster, I., and Jain, A.: Matminer: An open source toolkit for materials data mining. Comput. Mater. Sci. 152, 60 (2018).CrossRefGoogle Scholar
51.Broberg, D., Medasani, B., Zimmermann, N.E., Yu, G., Canning, A., Haranczyk, M., Asta, M., and Hautier, G.: PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators. Comput. Phys. Commun. 226, 165 (2018).CrossRefGoogle Scholar
52.van Rossum, G.: Scripting the Web with Python. World Wide Web J. 2, 97 (1997).Google Scholar
53.Gubernatis, J.E. and Lookman, T.: Machine learning in materials design and discovery: Examples from the present and suggestions for the future. Phys. Rev. Mater. 2, 120301 (2018).CrossRefGoogle Scholar
54.Rupp, M., Tkatchenko, A., Müller, K.-R., and von Lilienfeld, O.A.: Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108, 058301 (2012).CrossRefGoogle ScholarPubMed
55.Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., and Ramprasad, R.: Accelerating materials property predictions using machine learning. Sci. Rep. 3 (2013). http://doi.org/10.1038/srep02810CrossRefGoogle ScholarPubMed
56.Huan, T.D., Mannodi-Kanakkithodi, A., and Ramprasad, R.: Accelerated materials property predictions and design using motif-based fingerprints. Phys. Rev. B 92, 014106 (2015).CrossRefGoogle Scholar
57.Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B.P., Ramprasad, R., Gubernatis, J.E., and Lookman, T.: Machine learning bandgaps of double perovskites. Sci. Rep. 6 (2016). http://doi.org/10.1038/srep19375CrossRefGoogle ScholarPubMed
58.Mannodi-Kanakkithodi, A., Treich, G.M., Huan, T.D., Ma, R., Tefferi, M., Cao, Y., Sotzing, G.A., and Ramprasad, R.: Rational co-design of polymer dielectrics for energy storage. Adv. Mater. 28, 6277 (2016).CrossRefGoogle ScholarPubMed
59.Rosenblatt, F.: The perception - a probabilistic model for information-storage and organization in the brain. Psychol. Rev. 65, 386 (1958).CrossRefGoogle Scholar
60.Cortes, C. and Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273 (1995).CrossRefGoogle Scholar
61.De'ath, G. and Fabricius, K.: Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81, 3178 (2000).CrossRefGoogle Scholar
62.Rao, H. and Mukherjee, A.: Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites. Comput. Mater. Sci. 5, 307 (1996).CrossRefGoogle Scholar
63.Reich, Y. and Travitzky, N.: Machine learning of material behaviour knowledge from empirical data. Mater. Des. 16, 251 (1995).CrossRefGoogle Scholar
64.Chonghe, L., Jin, G., Pei, Q., Ruiliang, C., and Nianyi, C.: Some regularities of melting points of AB-type intermetallic compounds. J. Phys. Chem. Solids 57, 1797 (1996).CrossRefGoogle Scholar
65.Oliynyk, A.O. and Mar, A.: Discovery of intermetallic compounds from traditional to machine-learning approaches. Acc. Chem. Res. 51, 59 (2018). http://doi.org/10.1021/acs.accounts.7b00490.CrossRefGoogle ScholarPubMed
66.Carrete, J., Mingo, N., Wang, S., and Curtarolo, S.: Nanograined half-heusler semiconductors as advanced thermoelectrics: An ab initio high-throughput statistical study. Adv. Funct. Mater. 24, 7427 (2014).CrossRefGoogle Scholar
67.Carrete, J., Li, W., Mingo, N., Wang, S., and Curtarolo, S.: Finding unprecedentedly low-thermal-conductivity half-heusler semiconductors via high-throughput materials modeling. Phys. Rev. X 4, 011019 (2014).Google Scholar
68.Faber, F.A., Lindmaa, A., von Lilienfeld, O.A., and Armiento, R.: Machine learning energies of 2 million elpasolite (ABC 2D 6) crystals. Phys. Rev. Lett. 117, 135502 (2016).CrossRefGoogle Scholar
69.Jha, D., Ward, L., Paul, A., Liao, W.-K., Choudhary, A., Wolverton, C., and Agrawal, A.: ElemNet: deep learning the chemistry of materials from only elemental composition. Sci. Rep. 8, 17593 (2018). http://doi.org/10.1038/s41598-018-35934-yCrossRefGoogle ScholarPubMed
70.Xiang, X.D., Sun, X., Briceño, G., Lou, Y., Wang, K.-A., Chang, H., Wallace-Freedman, W.G., Chen, S.-W., and Schultz, P.G.: A combinatorial approach to materials discovery. Science 268, 1738 (1995).CrossRefGoogle ScholarPubMed
71.Armstrong, R.W., Combs, A.P., Tempest, P.A., Brown, S.D., and Keating, T.A.: Multiple-component condensation strategies for combinatorial library synthesis. Acc. Chem. Res. 29, 123 (1996). http://doi.org/10.1021/ar9502083CrossRefGoogle Scholar
72.Dudiy, S.V. and Zunger, A.: Searching for alloy configurations with target physical properties: Impurity design via a genetic algorithm inverse band structure approach. Phys. Rev. Lett. 97, 046401 (2006).CrossRefGoogle Scholar
73.Mannodi-Kanakkithodi, A., Pilania, G., Huan, T.D., Lookman, T., and Ramprasad, R.: Machine learning strategy for accelerated design of polymer dielectrics. Sci. Rep. 6, 20952 (2016). http://doi.org/10.1038/srep20952CrossRefGoogle ScholarPubMed
74.Ravindran, A., Ragsdell, K.M., and Reklaitis, G.V., Engineering Optimization: Method and Applications (John Wiley & Sons, Hoboken, NJ, 2006).CrossRefGoogle Scholar
75.Martoňák, R., Laio, A., and Parrinello, M.: Predicting crystal structures: The Parrinello-Rahman method revisited. Phys. Rev. Lett. 90, 075503 (2003).CrossRefGoogle ScholarPubMed
76.Pannetier, J., Bassas-Alsina, J., Rodriguez-Carvajal, J., and Caignaert, V.: Prediction of crystal-structures from crystal-chemistry rules by simulated annealing. Nature 346, 343 (1990).CrossRefGoogle Scholar
77.Wang, Y., Lv, J., Zhu, L., and Ma, Y.: Crystal structure prediction via particle-swarm optimization. Phys. Rev. B 82, 094116 (2010).CrossRefGoogle Scholar
78.Wang, Y., Lv, J., Zhu, L., and Ma, Y.: CALYPSO: A method for crystal structure prediction. Comput. Phys. Commun. 183, 2063 (2012).CrossRefGoogle Scholar
79.Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671 (1983).CrossRefGoogle ScholarPubMed
80.Wales, D.J. and Doye, J.P.K.: Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J. Phys. Chem. A 101, 5111 (1997). http://doi.org/10.1021/jp970984nCrossRefGoogle Scholar
81.Glass, C.W., Oganov, A.R., and Hansen, N.: USPEX—Evolutionary crystal structure prediction. Comput. Phys. Commun. 175, 713 (2006).CrossRefGoogle Scholar
82.Li, Y., Hao, J., Liu, H., Li, Y., and Ma, Y.: The metallization and superconductivity of dense hydrogen sulfide. J. Chem. Phys. 140, 174712 (2014). http://doi.org/10.1063/1.4874158.CrossRefGoogle ScholarPubMed
83.Semenok, D.V., Kvashnin, A.G., Kruglov, I.A., and Oganov, A.R.: Actinium hydrides AcH10, AcH12, AcH16 as high-temperature conventional superconductors. J. Phys. Chem. Lett. 9, 1920 (2018). http://doi.org/10.1021/acs.jpclett.8b00615.CrossRefGoogle ScholarPubMed
84.Patra, T.K., Meenakshisundaram, V., Hung, J.-H., and Simmons, D.S.: Neural-network-biased genetic algorithms for materials design: evolutionary algorithms that learn. ACS Comb. Sci. 19, 96 (2017). http://doi.org/10.1021/acscombsci.6b00136CrossRefGoogle ScholarPubMed
85.Botana, A.S., Zheng, H., Lapidus, S.H., Mitchell, J.F., and Norman, M.R.: Averievite: A copper oxide kagome antiferromagnet. Phys. Rev. B 98, 054421 (2018).CrossRefGoogle Scholar