Machine learning for perovskite materials design and discovery
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Abstract
Abstract The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.
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Research context
OpenAlex topics: Perovskite Materials and Applications · Machine Learning in Materials Science · Magnetic and transport properties of perovskites and related materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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1. Oró-Solé, J. et al. Synthesis, anion order and magnetic properties of RVO3−xNx perovskites (R= La, Pr, Nd; 0 ≤ x ≤ 1). J. Mater. Chem. C. 2, 2212–2220 (2014).10.1039/c3tc32362e ↗
2. Shiogai, J. et al. Signature of band inversion in the perovskite thin-film alloys BaSn1–xPbxO3. Phys. Rev. B 101, 125125 (2020).10.1103/PhysRevB.101.125125 ↗
3. Veldhuis, S. A. et al. Perovskite materials for light-emitting diodes and lasers. Adv. Mater. 28, 6804–6834 (2016).10.1002/adma.201600669 ↗
4. Wang, Y. et al. Mixed-dimensional self-assembly organic–inorganic perovskite microcrystals for stable and efficient photodetectors. Mater. Chem. C. 8, 5399–5408 (2020).10.1039/D0TC00686F ↗
5. Ekström, E. et al. The effects of microstructure, Nb content and secondary Ruddlesden–Popper phase on thermoelectric properties in perovskite CaMn1−xNbxO3 (x = 0-0.10) thin films. RSC Adv. 10, 7918–7926 (2020).10.1039/C9RA10007E ↗
6. Sydorchuk, V. et al. PrCo1−xFexO3 perovskite powders for possible photocatalytic applications. Res. Chem. Intermediat. 46, 1909–1930 (2020).10.1007/s11164-019-04071-0 ↗
7. Li, L. et al. A novel double-perovskite LiLaMgTeO6: Mn4+ far-red phosphor for indoor plant cultivation white LEDs: Crystal and electronic structure, and photoluminescence properties. J. Alloy. Compd. 832, 154905 (2020).10.1016/j.jallcom.2020.154905 ↗
8. Zhao, D. et al. Facile deposition of high-quality Cs2AgBiBr6 films for efficient double perovskite solar cells. Sci. China Mater. 63, 1518–1525 (2020).10.1007/s40843-020-1346-0 ↗
9. Graser, J., Kauwe, S. K. & Sparks, T. D. Machine learning and energy minimization approaches for crystal structure predictions: a review and new horizons. Chem. Mater. 30, 3601–3612 (2018).10.1021/acs.chemmater.7b05304 ↗
10. Rajan, K. Materials informatics. Mater. Today 8, 38–45 (2005).10.1016/S1369-7021(05)71123-8 ↗
11. Hohenberg, P. & Kohn, W. Inhomogeneous electron gas. Phys. Rev. 136, B864–B871 (1964).10.1103/PhysRev.136.B864 ↗
12. Hussain, A. et al. Monte Carlo simulation study of electron yields from compound semiconductor materials. J. Appl. Phys. 128, 015305 (2020).10.1063/5.0012154 ↗
13. Alder, B. J. & Wainwright, T. E. Studies in molecular dynamics. I. General method. J. Chem. Phy. 31, 459–466 (1959).10.1063/1.1730376 ↗
14. Agrawal, A. & Choudhary, A. Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science. APL Mater. 4, 053208 (2016).10.1063/1.4946894 ↗
15. Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73–76 (2016).10.1038/nature17439 ↗
16. Balachandran, P. V., Kowalski, B., Sehirlioglu, A. & Lookman, T. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat. Commun. 9, 1668 (2018).10.1038/s41467-018-03821-9 ↗
17. Dai, D. et al. Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys. Comput. Mater. Sci. 175, 109618 (2020).10.1016/j.commatsci.2020.109618 ↗
18. Sun, W. et al. Machine learning-assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials. Sci. Adv. 5, eaay4275 (2019).10.1126/sciadv.aay4275 ↗
19. Stanev, V. et al. Machine learning modeling of superconducting critical temperature. npj Comput. Mater. 4, 29 (2018).10.1038/s41524-018-0085-8 ↗
20. Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).10.1126/science.aaa8415 ↗
21. Rupp, M. Machine learning for quantum mechanics in a nutshell. Int. J. Quantum Chem. 115, 1058–1073 (2015).10.1002/qua.24954 ↗
22. Goldsmith, B. R., Esterhuizen, J., Liu, J. X., Bartel, C. J. & Sutton, C. Machine learning for heterogeneous catalyst design and discovery. AIChE J. 64, 2311–2323 (2018).10.1002/aic.16198 ↗
23. Lu, W., Xiao, R., Yang, J., Li, H. & Zhang, W. Data mining-aided materials discovery and optimization. J. Materiomics 3, 191–201 (2017).10.1016/j.jmat.2017.08.003 ↗
24. Wan, X. et al. Materials discovery and properties prediction in thermal transport via materials informatics: a mini review. Nano Lett. 19, 3387–3395 (2019).10.1021/acs.nanolett.8b05196 ↗
25. Chen, C. et al. A critical review of machine learning of energy materials. Adv. Energy Mater. 10, 1903242 (2020).10.1002/aenm.201903242 ↗
26. Liu, Y., Zhao, T., Ju, W. & Shi, S. Materials discovery and design using machine learning. J. Materiomics 3, 159–177 (2017).10.1016/j.jmat.2017.08.002 ↗
27. Toyao, T. et al. Machine learning for catalysis informatics: recent applications and prospects. ACS Catal. 10, 2260–2297 (2019).10.1021/acscatal.9b04186 ↗
28. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine learning in materials informatics: recent applications and prospects. npj Comput. Mater. 3, 54 (2017).10.1038/s41524-017-0056-5 ↗
29. Braham, E. J. et al. Machine learning-directed navigation of synthetic design space: a statistical learning approach to controlling the synthesis of perovskite halide nanoplatelets in the quantum-confined regime. Chem. Mater. 31, 3281–3292 (2019).10.1021/acs.chemmater.9b00212 ↗
30. Zhou, T., Song, Z. & Sundmacher, K. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design. Engineering 5, 1017–1026 (2019).10.1016/j.eng.2019.02.011 ↗
31. Orupattur, N. V., Mushrif, S. H. & Prasad, V. Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods. Comput. Mater. Sci. 174, 109474 (2020).10.1016/j.commatsci.2019.109474 ↗
32. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).10.1038/s41586-018-0337-2 ↗
33. Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5, 83 (2019).10.1038/s41524-019-0221-0 ↗
34. Wang, H., Ji, Y. & Li, Y. Simulation and design of energy materials accelerated by machine learning. WIREs Comput. Mol. Sci. 10, 1421 (2019).10.1002/wcms.1421 ↗
35. Zhou, Z. Machine Learning (Tsinghua University Press, Bei Jing, 2016).
36. Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).10.1214/ss/1177013815 ↗
37. Shi, L., Chang, D., Ji, X. & Lu, W. Using data mining to search for perovskite materials with higher specific surface area. J. Chem. Inf. Model. 58, 2420–2427 (2018).10.1021/acs.jcim.8b00436 ↗
38. Furmanchuk, A. et al. Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: a machine learning approach. J. Comput. Chem. 39, 191–202 (2018).10.1002/jcc.25067 ↗
39. Wang, Y. et al. Emerging perovskite materials for high density data storage and artificial synapses. J. Mater. Chem. C. 6, 1600–1617 (2018).10.1039/C7TC05326F ↗
40. Travis, W., Glover, E. N. K., Bronstein, H., Scanlon, D. O. & Palgrave, R. G. On the application of the tolerance factor to inorganic and hybrid halide perovskites: a revised system. Chem. Sci. 7, 4548–4556 (2016).10.1039/C5SC04845A ↗
41. Yin, W. J. et al. Oxide perovskites, double perovskites and derivatives for electrocatalysis, photocatalysis, and photovoltaics. Energ. Environ. Sci. 12, 442–462 (2019).10.1039/C8EE01574K ↗
42. Babu, R., Giribabu, L. & Singh, S. P. Recent advances in halide-based perovskite crystals and their optoelectronic applications. Cryst. Growth Des. 18, 2645–2664 (2018).10.1021/acs.cgd.7b01767 ↗
43. Körbel, S., Marques, M. A. L. & Botti, S. Stability and electronic properties of new inorganic perovskites from high-throughput ab initio calculations. J. Mater. Chem. C. 4, 3157–3167 (2016).10.1039/C5TC04172D ↗
44. Saha-Dasgupta, T. Magnetism in double perovskites. J. Supercond. Nov. Magn. 26, 1991–1995 (2012).10.1007/s10948-012-1920-7 ↗
45. Li, C. et al. A progressive learning method for predicting the band gap of ABO3 perovskites using an instrumental variable. J. Mater. Chem. C. 8, 3127–3136 (2020).10.1039/C9TC06632B ↗
46. Sun, Z. et al. Research progress and perspective of machine learning in material design (in Chinese). Chin. Sci. B-Chin. 64, 3270–3275 (2019).
47. Bally, M. A. A. & Khan, F. A. Structural, dielectric and magnetic properties of La0.55Sr0.45MnO3 polycrystalline perovskite. J. Magn. Magn. Mater. 509, 166897 (2020).10.1016/j.jmmm.2020.166897 ↗
48. Shanker, J., Kumar, R. V., Rao, G. N. & Babu, D. S. Magnetic reversal in Fe substituted NdCrO3 perovskite nanoparticles. Mater. Chem. Phys. 251, 123098 (2020).10.1016/j.matchemphys.2020.123098 ↗
49. AboZied, A. E. R. T., Ghani, A. A., Ali, A. I. & Salaheldin, T. A. Structure, magnetic and magnetocaloric properties of nano crystalline perovskite La0.8Ag0.2MnO3. J. Magn. Magn. Mater. 479, 260–267 (2019).10.1016/j.jmmm.2019.02.043 ↗
50. Mechi, N. et al. La0.6Ca0.2Na0.2MnO3 perovskite: structural, magnetic, critical, and magnetocaloric properties. J. Supercond. Nov. Magn. 33, 1385–1393 (2019).10.1007/s10948-019-05353-9 ↗
51. Li, L. et al. An efficient and durable perovskite electrocatalyst for oxygen reduction in solid oxide fuel cells. Chem. Eng. J. 396, 125237 (2020).10.1016/j.cej.2020.125237 ↗
52. Xia, W., Li, Q., Sun, L., Huo, L. & Zhao, H. Electrochemical performance of Sn-doped Bi0.5Sr0.5FeO3-δ perovskite as cathode electrocatalyst for solid oxide fuel cells. J. Alloy. Compd. 835, 155406 (2020).10.1016/j.jallcom.2020.155406 ↗
53. Carrasco-Jaim, O. A., Huerta-Flores, A. M., Torres-Martínez, L. M. & Moctezuma, E. Fast in-situ photodeposition of Ag and Cu nanoparticles onto AgTaO3 perovskite for an enhanced photocatalytic hydrogen generation. Int. J. Hydrog. Energ. 45, 9744–9757 (2020).10.1016/j.ijhydene.2020.01.242 ↗
54. Zhou, J. et al. Photocatalytic degradation characteristics of tetracycline and structural transformation on bismuth silver oxide perovskite nano-catalysts. Appl. Nanosci. 10, 2329–2338 (2020).10.1007/s13204-020-01440-9 ↗
55. Goldschmidt, V. M. Die Gesetze der Krystallochemie. Naturwissenschaften 14, 477–485 (1926).10.1007/BF01507527 ↗
56. Sun, Q. & Yin, W. J. Thermodynamic stability trend of cubic perovskites. J. Am. Chem. Soc. 139, 14905–14908 (2017).10.1021/jacs.7b09379 ↗
57. Bartel, C. J. et al. New tolerance factor to predict the stability of perovskite oxides and halides. Sci. Adv. 5, eaav0693 (2019).10.1126/sciadv.aav0693 ↗
58. Armiento, R., Kozinsky, B., Hautier, G., Fornari, M. & Ceder, G. High-throughput screening of perovskite alloys for piezoelectric performance and thermodynamic stability. Phys. Rev. B 89, 134103 (2014).10.1103/PhysRevB.89.134103 ↗
59. Liu, M. et al. Spinel compounds as multivalent battery cathodes: a systematic evaluation based on ab initio calculations. Energy Environ. Sci. 8, 964–974 (2015).10.1039/C4EE03389B ↗
60. Schmidt, J. et al. Predicting the thermodynamic stability of solids combining density functional theory and machine learning. Chem. Mater. 29, 5090–5103 (2017).10.1021/acs.chemmater.7b00156 ↗
61. Li, W., Jacobs, R. & Morgan, D. Predicting the thermodynamic stability of perovskite oxides using machine learning models. Comput. Mater. Sci. 150, 454–463 (2018).10.1016/j.commatsci.2018.04.033 ↗
62. Liu, H. et al. Screening stable and metastable ABO3 perovskites using machine learning and the materials project. Comput. Mater. Sci. 177, 109614 (2020).10.1016/j.commatsci.2020.109614 ↗
63. Li, X. et al. Computational screening of new perovskite materials using transfer learning and deep learning. Appl. Sci. 9, 5510 (2019).10.3390/app9245510 ↗
64. Pilania, G., Balachandran, P. V., Kim, C. & Lookman, T. Finding new perovskite halides via machine learning. Front. Mater. 3, 19 (2016).10.3389/fmats.2016.00019 ↗
65. Balachandran, P. V. et al. Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory. Phys. Rev. Mater. 2, 043802 (2018).10.1103/PhysRevMaterials.2.043802 ↗
66. Jain, D., Chaube, S., Khullar, P., Goverapet Srinivasan, S. & Rai, B. R. Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases. Phys. Chem. Chem. Phys. 21, 19423–19436 (2019).10.1039/C9CP03240A ↗
67. Park, H. et al. Learn-and-match molecular cations for perovskites. J. Phys. Chem. A 123, 7323–7334 (2019).10.1021/acs.jpca.9b06208 ↗
68. Gladkikh, V. et al. Machine learning for predicting the band gaps of ABX3 perovskites from elemental properties. J. Phys. Chem. C. 124, 8905–8918 (2020).10.1021/acs.jpcc.9b11768 ↗
69. Takahashi, K., Takahashi, L., Miyazato, I. & Tanaka, Y. Searching for hidden perovskite materials for photovoltaic systems by combining data science and first principle calculations. ACS Photonics 5, 771–775 (2018).10.1021/acsphotonics.7b01479 ↗
70. Chen, Y. et al. Correlation of dielectric dispersion with distributed Curie temperature in relaxor ferroelectrics. J. Appl. Phys. 125, 184104 (2019).10.1063/1.5080988 ↗
71. Luo, Z. et al. Growth and characterization of ternary BiScO3-Pb(Cd1/3Nb2/3)O3-PbTiO3 ferroelectric single crystals with high Curie temperature. CrystEngComm 22, 4544–4551 (2020).10.1039/D0CE00473A ↗
72. Shi, T., Li, G. & Zhu, J. Compositional design strategy for high performance ferroelectric oxides with perovskite structure. Ceram. Int. 43, 2910–2917 (2017).10.1016/j.ceramint.2016.11.085 ↗
73. Jin, F., Zhang, H. & Chen, Q. Improved Curie temperature and temperature coefficient of resistance (TCR) in La0.7Ca0.3-xSrMnO3: Ag0.2 composites. J. Alloy. Compd. 747, 1027–1032 (2018).10.1016/j.jallcom.2018.03.117 ↗
74. Pang, D. et al. Lead-reduced Bi(Ni2/3Ta1/3)O3-PbTiO3 perovskite ceramics with high Curie temperature and performance. J. Am. Ceram. Soc. 102, 1227–1239 (2019).10.1111/jace.15962 ↗
75. Zhai, X., Chen, M. & Lu, W. Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods. Comp. Mater. Sci. 151, 41–48 (2018).10.1016/j.commatsci.2018.04.031 ↗
76. Yang, Z. X. et al. High critical transition temperature of lead-based perovskite ferroelectric crystals: a machine learning study. Acta Phys. Sin. 68, 210502 (2019).10.7498/aps.68.20190942 ↗
77. Amit, E., Keren, A., Lord, J. S. & King, P. A precise measurement of the oxygen isotope effect on the Néel temperature in cuprates. Adv. Cond. Matter Phys. 2011, 1–5 (2011).10.1155/2011/178190 ↗
78. Chmaissem, O. et al. Relationship between structural parameters and the Néel temperature in Sr1-xCaxMnO3(0 ≤ x ≤ 1) and Sr1-yBayMnO3(y ≤ 0.2). Phys. Rev. B 64, 134412 (2001).10.1103/PhysRevB.64.134412 ↗
79. Xiao, L., Zhang, Q., Xu, X., Ji, X. & Lu, W. Support vector regression assisted predictions the néel temperature of perovskites manganites. Comp. Appl. Chem. 35, 349–357 (2018).
80. Gutfleisch, O. et al. Magnetic materials and devices for the 21st century: stronger, lighter, and more energy efficient. Adv. Mater. 23, 821–842 (2011).10.1002/adma.201002180 ↗
81. Cao, G. et al. Enhanced magnetic entropy change and refrigeration capacity of La(Fe,Ni)11.5Si1.5 alloys through vacuum annealing treatment. J. Alloy. Compd. 800, 363–371 (2019).10.1016/j.jallcom.2019.06.069 ↗
82. Phan, M. H. & Yu, S. C. Review of the magnetocaloric effect in manganite materials. J. Magn. Magn. Mater. 308, 325–340 (2007).10.1016/j.jmmm.2006.07.025 ↗
83. Cao, F. et al. Effect of yttrium doping on magnetic properties and magnetic entropy change of bilayered perovskite La1.3Sr1.7Mn2O7. J. Low. Temp. Phys. 200, 16–25 (2020).10.1007/s10909-020-02457-8 ↗
84. Chen, F. et al. Large magnetic entropy change and refrigeration capacity around room temperature in quinary Ni41Co9-xFexMn40Sn10 alloys (x = 2.0 and 2.5). J. Alloy. Compd. 825, 154053 (2020).10.1016/j.jallcom.2020.154053 ↗
85. Zhang, Y. & Xu, X. Machine learning the magnetocaloric effect in manganites from lattice parameters. Appl. Phys. A 126, 341 (2020).10.1007/s00339-020-03503-8 ↗
86. Xu, W. et al. Dielectric breakdown strength of alumina ceramics reinforced by fractal dendritic Ca9Al(PO4)7 as the second crystalline phase. J. Alloy. Compd. 832, 154811 (2020).10.1016/j.jallcom.2020.154811 ↗
87. Yao, T. et al. Nano-BN encapsulated micro-AlN as fillers for epoxy composites with high thermal conductivity and sufficient dielectric breakdown strength. IEEE Trans. Dielectr. Electr. Insul. 27, 528–534 (2020).10.1109/TDEI.2020.008606 ↗
88. Zhang, T. et al. Effect of pores on dielectric breakdown strength of alumina ceramics via surface and volume effects. J. Eur. Ceram. Soc. 40, 3019–3026 (2020).10.1016/j.jeurceramsoc.2020.03.024 ↗
89. Lu, Y. et al. Remarkable dielectric breakdown strength enhancement of a PVDF terpolymer using a 2D hybrid organic inorganic perovskite as a functional additive. J. Mater. Chem. C. 7, 13390–13395 (2019).10.1039/C9TC03220G ↗
90. Yang, L. et al. Perovskite lead-free dielectrics for energy storage applications. Prog. Mater. Sci. 102, 72–108 (2019).10.1016/j.pmatsci.2018.12.005 ↗
91. Kim, C., Pilania, G. & Ramprasad, R. Machine learning assisted predictions of intrinsic dielectric breakdown strength of ABX3 perovskites. J. Phys. Chem. C. 120, 14575–14580 (2016).10.1021/acs.jpcc.6b05068 ↗
92. Zhang, C. et al. Achieving ultrahigh dielectric breakdown strength in MgO-based ceramics by composite structure design. Mater. Chem. C. 7, 8120–8130 (2019).10.1039/C9TC02197C ↗
93. Gao, J. et al. Designing high dielectric permittivity material in barium titanate. J. Phys. Chem. C. 121, 13106–13113 (2017).10.1021/acs.jpcc.7b04636 ↗
94. Bhattacharyya, R., Das, S. & Omar, S. High ionic conductivity of Mg2+-doped non-stoichiometric sodium bismuth titanate. Acta Mater. 159, 8–15 (2018).10.1016/j.actamat.2018.08.007 ↗
95. Reis, S. L. & Muccillo, E. N. S. Influence of small amounts of gallium oxide addition on ionic conductivity of La0.9Sr0.1Ga0.8Mg0.2O3-δ solid electrolyte. Ceram. Int. 44, 115–119 (2018).10.1016/j.ceramint.2017.09.139 ↗
96. Verma, O. N., Jha, P. A., Singh, P., Jha, P. K. & Singh, P. Influence of iso-valent ‘Sm’ double substitution on the ionic conductivity of La0.9Sr0.1Al0.9Mg0.1O3-δ ceramic system. Mater. Chem. Phys. 241, 122345 (2020).10.1016/j.matchemphys.2019.122345 ↗
97. Liu, X., Lu, W., Peng, C., Sun, Q. & Guo, J. Two semi-empirical approaches for the prediction of oxide ionic conductivities in ABO3 perovskites. Comput. Mater. Sci. 46, 860–868 (2009).10.1016/j.commatsci.2009.03.011 ↗
98. Kaneko, M., Fujii, M., Hisatomi, T., Yamashita, K. & Domen, K. Regression model for stabilization energies associated with anion ordering in perovskite-type oxynitrides. J. Energy Chem. 36, 7–14 (2019).10.1016/j.jechem.2019.01.012 ↗
99. Zheng, W. D. et al. Performance prediction of perovskite materials based on different machine learning algorithms. Chin. J. Nonfer. Met. 29, 803–807 (2019).10.1016/S1003-6326(19)65161-7 ↗
100. Lu, S., Zhou, Q., Ma, L., Guo, Y. & Wang, J. Rapid discovery of ferroelectric photovoltaic perovskites and material descriptors via machine learning. Small Methods 3, 1900360 (2019).10.1002/smtd.201900360 ↗