logo

Chinese Science Bulletin, Volume 64 , Issue 32 : 3270-3275(2019) https://doi.org/10.1360/TB-2019-0456

Research progress and perspective of machine learning in material design

More info
  • ReceivedAug 3, 2019
  • AcceptedOct 8, 2019
  • PublishedOct 11, 2019

Abstract


Funded by

国家重点研发计划(2016YFB0700700)

国家自然科学基金(11674237,11974257,51602211)


References

[1] Hattrick-Simpers J R, Gregoire J M, Kusne A G. Perspective: Composition-structure-property mapping in high-throughput experiments: Turning data into knowledge. APL Mater, 2016, 4: 053211 CrossRef ADS Google Scholar

[2] Butler K T, Davies D W, Cartwright H, et al. Machine learning for molecular and materials science. Nature, 2018, 559: 547-555 CrossRef PubMed ADS Google Scholar

[3] Agrawal A, Choudhary A. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater, 2016, 4: 053208 CrossRef ADS Google Scholar

[4] Hill J, Mulholland G, Persson K, et al. Materials science with large-scale data and informatics: Unlocking new opportunities. MRS Bull, 2016, 41: 399-409 CrossRef Google Scholar

[5] Schmidt J, Marques M R G, Botti S, et al. Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater, 2019, 5: 83 CrossRef ADS Google Scholar

[6] Ramprasad R, Batra R, Pilania G, et al. Machine learning in materials informatics: Recent applications and prospects. npj Comput Mater, 2017, 3: 54 CrossRef ADS arXiv Google Scholar

[7] Tetko I V, Maran U, Tropsha A. Public (Q)SAR services, integrated modeling environments, and model repositories on the web: State of the art and perspectives for future development. Mol Inf, 2017, 36: 1600082 CrossRef PubMed Google Scholar

[8] Kalidindi S R, Brough D B, Li S, et al. Role of materials data science and informatics in accelerated materials innovation. MRS Bull, 2016, 41: 596-602 CrossRef Google Scholar

[9] Correa-Baena J P, Hippalgaonkar K, van Duren J, et al. Accelerating materials development via automation, machine learning, and high-performance computing. Joule, 2018, 2: 1410-1420 CrossRef Google Scholar

[10] Brunton S L, Kutz J N. Methods for data-driven multiscale model discovery for materials. J Phys Mater, 2019, 2: 044002 CrossRef ADS Google Scholar

[11] Schleder G R, Padilha A C M, Acosta C M, et al. From DFT to machine learning: Recent approaches to materials science–A review. J Phys Mater, 2019, 2: 032001. Google Scholar

[12] Raccuglia P, Elbert K C, Adler P D F, et al. Machine-learning-assisted materials discovery using failed experiments. Nature, 2016, 533: 73-76 CrossRef PubMed ADS Google Scholar

[13] Yu Y, Tan X, Ning S, et al. Machine learning for understanding compatibility of organic-inorganic hybrid perovskites with post-treatment amines. ACS Energy Lett, 2019, 4: 397-404 CrossRef Google Scholar

[14] Ward L, Wolverton C. Atomistic calculations and materials informatics: A review. Curr Opin Solid State Mater Sci, 2017, 21: 167-176 CrossRef ADS Google Scholar

[15] Belsky A, Hellenbrandt M, Karen V L, et al. New developments in the inorganic crystal structure database (ICSD): Accessibility in support of materials research and design. Acta Cryst Sect A Found Cryst, 2002, 58: 364-369 CrossRef PubMed Google Scholar

[16] Allen F H. The cambridge structural database: A quarter of a million crystal structures and rising. Acta Cryst Sect B Struct Sci Cryst Eng Mater, 2002, 58: 380−388. Google Scholar

[17] Gražulis S, Chateigner D, Downs R T, et al. Crystallography Open Database – An open-access collection of crystal structures. J Appl Crystlogr, 2009, 42: 726-729 CrossRef PubMed Google Scholar

[18] Villars P, Berndt M, Brandenburg K, et al. The pauling file, binaries edition. J Alloys Compd, 2004, 367: 293-297 CrossRef Google Scholar

[19] Xu Y, Yamazaki M, Villars P. Inorganic materials database for exploring the nature of material. Jpn J Appl Phys, 2011, 50: 11RH02 CrossRef Google Scholar

[20] Kirklin S, Saal J E, Meredig B, et al. The Open Quantum Materials Database (OQMD): Assessing the accuracy of DFT formation energies. npj Comput Mater, 2015, 1: 15010 CrossRef ADS Google Scholar

[21] Hachmann J, Olivares-Amaya R, Atahan-Evrenk S, et al. The harvard clean energy project: Large-scale computational screening and design of organic photovoltaics on the world community grid. J Phys Chem Lett, 2011, 2: 2241−2251. Google Scholar

[22] Curtarolo S, Setyawan W, Hart G L W, et al. AFLOW: An automatic framework for high-throughput materials discovery. Comput Mater Sci, 2012, 58: 218-226 CrossRef Google Scholar

[23] Liu Y, Zhao T, Ju W, et al. Materials discovery and design using machine learning. J Materiom, 2017, 3: 159-177 CrossRef Google Scholar

[24] Ward L, Agrawal A, Choudhary A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput Mater, 2016, 2: 16028 CrossRef Google Scholar

[25] Peña M A, Fierro J L G. Chemical structures and performance of perovskite oxides. Chem Rev, 2001, 101: 1981-2018 CrossRef Google Scholar

[26] Yin W J, Weng B, Ge J, et al. Oxide perovskites, double perovskites and derivatives for electrocatalysis, photocatalysis, and photovoltaics. Energy Environ Sci, 2019, 12: 442-462 CrossRef Google Scholar

[27] Roth R S. Classification of perovskite and other ABO3-type compounds. J Res Nat Bur Stand, 1957, 58: 75−88. Google Scholar

[28] Zhang H, Li N, Li K, et al. Structural stability and formability of ABO3-type perovskite compounds. Acta Cryst Sect A Found Cryst, 2007, 63: 812-818 CrossRef PubMed Google Scholar

[29] Li C, Lu X, Ding W, et al. Formability of ABX3 (X = F, Cl, Br, I) halide perovskites. Acta Crystlogr B Struct Sci, 2008, 64: 702-707 CrossRef PubMed Google Scholar

[30] Balachandran P V, Emery A A, Gubernatis J E, et al. Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory. Phys Rev Mater, 2018, 2: 043802 CrossRef ADS Google Scholar

[31] Li W, Jacobs R, Morgan D. Predicting the thermodynamic stability of perovskite oxides using machine learning models. Comput Mater Sci, 2018, 150: 454-463 CrossRef Google Scholar

[32] Xu Q, Li Z, Liu M, et al. Rationalizing perovskite data for machine learning and materials design. J Phys Chem Lett, 2018, 9: 6948-6954 CrossRef PubMed Google Scholar

[33] Lu S, Zhou Q, Ouyang Y, et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat Commun, 2018, 9: 3405 CrossRef PubMed ADS Google Scholar

[34] Li Z, Xu Q, Sun Q, et al. Thermodynamic stability landscape of halide double perovskites via high-throughput computing and machine learning. Adv Funct Mater, 2019, 29: 1807280 CrossRef Google Scholar

[35] Sun Q, Yin W J. Thermodynamic stability trend of cubic perovskites. J Am Chem Soc, 2017, 139: 14905-14908 CrossRef PubMed Google Scholar

[36] Tran K, Ulissi Z W. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat Catal, 2018, 1: 696-703 CrossRef Google Scholar

[37] Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel T D, et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat Mater, 2016, 15: 1120−1127. Google Scholar

[38] Warmuth M K, Liao J, Rätsch G, et al. Active learning with support vector machines in the drug discovery process. J Chem Inf Comput Sci, 2003, 43: 667-673 CrossRef PubMed Google Scholar

[39] Gubaev K, Podryabinkin E V, Shapeev A V. Machine learning of molecular properties: Locality and active learning. J Chem Phys, 2018, 148: 1−9. Google Scholar

[40] Weng B, Song Z, Zhu R, et al. Symbolic regression discovery of new perovskite catalysts with high oxygen evolution reaction activity. 2019, arXiv:1908.06778. Google Scholar

[41] Waag W, Fleischer C, Sauer D U. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J Power Sources, 2014, 258: 321-339 CrossRef ADS Google Scholar

[42] Wu L, Fu X, Guan Y. Review of the remaining useful life prognostics of vehicle lithium-ion batteries using data-driven methodologies. Appl Sci, 2016, 6: 166 CrossRef Google Scholar

[43] Severson K A, Attia P M, Jin N, et al. Data-driven prediction of battery cycle life before capacity degradation. Nat Energy, 2019, 4: 383-391 CrossRef ADS Google Scholar

[44] Wolpert D H, Macready W G. No free lunch theorems for optimization. IEEE Trans Evol Comput, 1996, 1: 67. Google Scholar

[45] Wang Y, Wagner N, Rondinelli J M. Symbolic regression in materials science. MRC Commun, 2019, 9: 793-805 CrossRef Google Scholar

[46] Bartel C J, Sutton C, Goldsmith B R, et al. New tolerance factor to predict the stability of perovskite oxides and halides. Sci Adv, 2019, 5: eaav0693 CrossRef PubMed ADS arXiv Google Scholar

[47] Bartel C J, Millican S L, Deml A M, et al. Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry. Nat Commun, 2018, 9: 4168 CrossRef PubMed ADS arXiv Google Scholar

[48] Jankowski N, Duch W, Grąbczewski K. Meta-learning in Computational Intelligence. Berlin: Springer, 2011. Google Scholar

[49] Graves A, Wayne G, Danihelka I. Neural turing machines. 2014, arXiv:1410.5401. arXiv Google Scholar

[50] Duan Y, Andrychowicz M, Stadie B, et al. One-shot imitation learning. In: Guyon I, Luxburg U V, Bengio S, et al, eds. Advances in Neural Information Processing Systems 30 (NIPS 2017). Red Hook: NIPS, 2017. Google Scholar

[51] Lake B M, Salakhutdinov R, Tenenbaum J B. Human-level concept learning through probabilistic program induction. Science, 2015, 350: 1332-1338 CrossRef PubMed ADS Google Scholar

[52] Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science, 2018, 361: 360-365 CrossRef PubMed ADS Google Scholar

[53] Zubatyuk R, Smith J S, Leszczynski J, et al. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Sci Adv, 2019, 5: eaav6490 CrossRef PubMed Google Scholar