DataVXAdvanced Composites and Hybrid MaterialsPaper
journal article·2022-12

Applications of machine learning in perovskite materials

Ziman Wang, Ming Yang, Xixi Xie, Chenyang Yu, Qinglong Jiang, Mina Huang, Hassan Algadi, Zhanhu Guo, Hang Zhang

Advanced Composites and Hybrid Materials· Volume 5· Issue 4
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Abstract

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Electronic materials

OpenAlex topics: Perovskite Materials and Applications · Quantum Dots Synthesis And Properties · Machine Learning in Materials Science

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References (66)

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1. Kojima A, Teshima K, Shirai Y et al (2009) Organometal halide perovskites as visible-light sensitizers for photovoltaic cells. J Am Chem Soc 131:6050–6051. https://doi.org/10.1021/ja809598r10.1021/ja809598r ↗
2. Green MA, Dunlop ED, Hohl-Ebinger J et al (2019) Solar cell efficiency tables (Version 55). Prog Photovolt 28:3–15. https://doi.org/10.1002/pip.322810.1002/pip.3228 ↗
3. Rostalski J, Meissner D (2000) Monochromatic versus solar efficiencies of organic solar cells. Sol Energy Mater Sol Cells 61:87–95. https://doi.org/10.1016/S0927-0248(99)00099-910.1016/S0927-0248(99)00099-9 ↗
4. Mickelsen RA, Chen S (1981) Development of a 9.4% efficient thin-film CuInSe2/CdS solar cell. Proc 15th IEEE Photovolt Special Conf 15:800–804
5. Kamada R, Yagioka T, Adachi S et al (2016) New world record Cu(In,Ga)(Se,S)2 Thin film solar cell efficiency beyond 22%. Proc 43rd IEEE Photovoltaic Specialists Conference 1287–129110.1109/PVSC.2016.7749822 ↗
6. Blakers A, Zin N, McIntosh KR et al (2013) High efficiency silicon solar cells. PV Asia Pacific Conference 33:1–10. https://doi.org/10.1016/j.egypro.2013.05.03310.1016/j.egypro.2013.05.033 ↗
7. Liu K, Yang H, Wang W et al (2021) Numerical and experimental exploration towards a 26% efficiency rear-junction n-type silicon solar cell with front local-area and rear full-area polysilicon passivated contacts. Sol Energy 221:1–9. https://doi.org/10.1016/j.solener.2021.04.02010.1016/j.solener.2021.04.020 ↗
8. Tao Q, Xu P, Li M et al (2021) Machine learning for perovskite materials design and discovery. NPJ Comput Mater 7. https://doi.org/10.1038/s41524-021-00495-810.1038/s41524-021-00495-8 ↗
9. Tanaka I (2020) Data-driven materials discovery from large chemistry spaces. Matter 3:327–328. https://doi.org/10.1016/j.matt.2020.07.01010.1016/j.matt.2020.07.010 ↗
10. Zhang H, Hippalgaonkar K, Buonassisi T et al (2018) Machine learning for novel thermal-materials discovery: early successes, opportunities, and challenges. ES Energy & Environment 2:1–8. https://doi.org/10.30919/esee8c20910.30919/esee8c209 ↗
11. Kumbhar A, Dhawale PG, Kumbhar S et al (2021) A comprehensive review: machine learning and its application in integrated power system. Energ Rep 7:5467–5474. https://doi.org/10.1016/j.egyr.2021.08.13310.1016/j.egyr.2021.08.133 ↗
12. Zhou ZH (2016) Machine Learning. Tsinghua University Press, Beijing
13. Li C, Hao H, Xu B et al (2020) A progressive learning method for predicting the band gap of ABO3 perovskites using an instrumental variables. J Mater Chem C 8:3127–3136. https://doi.org/10.1039/c9tc06632b10.1039/c9tc06632b ↗
14. Allam O, Holmes C, Greenberg Z et al (2018) Density functional theory-machine learning approach to analyze band gap of elemental halide perovskites and Ruddlesden-Popper Phases. Chemphyschem 19:2559–2565. https://doi.org/10.1002/cphc.20180038210.1002/cphc.201800382 ↗
15. Park H, Mall R, Ali A et al (2020) Importance of structural deformation features in the prediction of hybrid perovskite bandgaps. Comput Mater Sci 184:109858. https://doi.org/10.1016/j.commatsci.2020.10985810.1016/j.commatsci.2020.109858 ↗
16. Pilania G, Mannodi-Kanakkithodi A, Uberuaga BP et al (2016) Machine learning bandgaps of double perovskites. Sci Rep 6. https://doi.org/10.1038/srep1937510.1038/srep19375 ↗
17. Yang X, Li L, Tao Q et al (2021) Rapid discovery of narrow bandgap oxide double perovskites using machine learning. Comput Mater Sci 196:110528. https://doi.org/10.1016/j.commatsci.2021.11052810.1016/j.commatsci.2021.110528 ↗
18. Chaube S, Khullar P, Srinvasan SG et al (2019) A statistical learning framework for accelerated bandgap prediction of inorganic compounds. J Electron Mater 49:752–762. https://doi.org/10.1007/s11664-019-07779-210.1007/s11664-019-07779-2 ↗
19. Omprakash P, Manikandan B, Sandeep A et al (2021) Graph representational learning for bandgap prediction in varied perovskite crystals. Comput Mater Sci 196:110530. https://doi.org/10.1016/j.commatsci.2021.11053010.1016/j.commatsci.2021.110530 ↗
20. Li W, Jacobs R, Morgan D (2018) Predicting the thermodynamic stability of perovskite oxides using machine learning models. Comput Mater Sci 150:454–463. https://doi.org/10.1016/j.commatsci.2018.04.03310.1016/j.commatsci.2018.04.033 ↗
21. Liu H, Cheng J, Dong H et al (2020) Screening stable and metastable ABO3 perovskites using machine learning and the materials project. Comput Mater Sci 177:109614. https://doi.org/10.1016/j.commatsci.2020.10961410.1016/j.commatsci.2020.109614 ↗
22. Talapatra A, Uberuaga BP, Stanek CR et al (2021) A machine learning approach for the prediction of formability and thermodynamic stability of single and double perovskite oxides. Chem Mater 33:845–858. https://doi.org/10.1021/acs.chemmater.0c0340210.1021/acs.chemmater.0c03402 ↗
23. Maddah HA, Berry V, Behura SK (2020) Cuboctahedral stability in titanium halide perovskites via machine learning. Comput Mater Sci 173:109415. https://doi.org/10.1016/j.commatsci.2019.10941510.1016/j.commatsci.2019.109415 ↗
24. Zhao Y, Zhang J, Xu Z et al (2021) Discovery of temperature-induced stability reversal in perovskites using high-throughput robotic learning. Nat Commun 12:2191. https://doi.org/10.1038/s41467-021-22472-x10.1038/s41467-021-22472-x ↗
25. Hillhouse HW, Dunlap-Shohl WA, Stoddard RJ (2020) Quantitative prediction of perovskite stability using accelerated testing and machine learning. 47th IEEE Photovoltaic Specialists Conference:2116–2119. https://doi.org/10.1109/PVSC45281.2020.930093110.1109/PVSC45281.2020.9300931 ↗
26. Roekeghem A, van Carrete J, Oses C et al (2016) High-throughput computation of thermal conductivity of high-temperature solid phases: the case of oxide and fluoride perovskites. Phys Rev X 6. https://doi.org/10.1103/PhysRevX.6.04106110.1103/PhysRevX.6.041061 ↗
27. Schmidt J, Shi J, Borlido P et al (2017) Predicting the thermodynamic stability of solids combining density functional theory and machine learning. Chem Mater 29:5090–5103. https://doi.org/10.1021/acs.chemmater.7b0015610.1021/acs.chemmater.7b00156 ↗
28. Yang M, Zhang X, Zhang H (2021) Effects of monovacancy on thermal properties of bilayer graphene nanoribbons by molecular dynamics simulations. J Therm Sci:1–8. https://doi.org/10.1007/s11630-021-1412-910.1007/s11630-021-1412-9 ↗
29. Wang Z, Yang M, Jiang Q et al (2022) Improving the thermoelectric properties of 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene-based organic semiconductors by isotropic strain. ES Mater Manuf 16:66–77. https://doi.org/10.30919/esmm5f48910.30919/esmm5f489 ↗
30. Priya P, Aluru NR (2021) Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning. NPJ Comput Mater 7. https://doi.org/10.1038/s41524-021-00551-310.1038/s41524-021-00551-3 ↗
31. Liu X, Lu W, Peng C et al (2009) Two semi-empirical approaches for the prediction of oxide ionic conductivities in ABO3 perovskites. Comput Mater Sci 46:860–868. https://doi.org/10.1016/j.commatsci.2009.04.04710.1016/j.commatsci.2009.04.047 ↗
32. Li L, You Y, Hu S et al (2019) Electronic transport of organic-inorganic hybrid perovskites from first-principles and machine learning. Appl Phys Lett 114:083102. https://doi.org/10.1063/1.504551210.1063/1.5045512 ↗
33. Zhou G, Chu W, Prezhdo OV (2020) Structure deformation controls charge losses in MAPbI3: unsupervised machine learning of nonadiabatic molecular dynamics. ACS Energy Lett 5:1930–1938. https://doi.org/10.1021/acsenergylett.0c0089910.1021/acsenergylett.0c00899 ↗
34. Li Z, Achenie LEK, Xin H (2020) An adaptive machine learning strategy for accelerating discovery of perovskite electrocatalysts. ACS Catal 10:4377–4384. https://doi.org/10.1021/acscatal.9b0524810.1021/acscatal.9b05248 ↗
35. Wang X, Xiao B, Li Y et al (2020) First-principles based machine learning study of oxygen evolution reactions of perovskite oxides using a surface center-environment feature model. Appl Surf Sci 531:147323. https://doi.org/10.1016/j.apsusc.2020.14732310.1016/j.apsusc.2020.147323 ↗
36. Tao Q, Lu T, Sheng Y (2021) Machine learning aided design of perovskite oxide materials for photocatalytic water splitting. J Energ Chem 60:351–359. https://doi.org/10.1016/j.jechem.2021.01.03510.1016/j.jechem.2021.01.035 ↗
37. Weng B, Song Z, Zhu R et al (2020) Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts. Nat Commun: 11. https://doi.org/10.1038/s41467-020-17263-910.1038/s41467-020-17263-9 ↗
38. Zhang G, Fan B, Zhao P et al (2018) Ferroelectric polymer nanocomposites with complementary nanostructured fillers for electrocaloric cooling with high power density and great efficiency. ACS Appl Energy Mater (3):1344–1354. https://doi.org/10.1021/acsaem.8b0005210.1021/acsaem.8b00052 ↗
39. Wang Z, Gao Y, Ma Y et al (2021) Enhanced electrocaloric effect within a broad temperature range in lead-free polymer composite films by blending the rare-earth doped BaTiO3 nanopowders. Adv Comp Hybr Mater 4(3):469–477. https://doi.org/10.1007/s42114-021-00252-x10.1007/s42114-021-00252-x ↗
40. Wang Z, Yang M, Zhang H (2021) Strain engineering on electrocaloric effect in PbTiO3 and BaTiO3. Adv Comp Hybr Mater 4(4):1239–1247. https://doi.org/10.1007/s42114-021-00257-610.1007/s42114-021-00257-6 ↗
41. Min K, Cho E (2020) Accelerated discovery of potential ferroelectric perovskite via active learning. J Mater Chem C 8(23). https://doi.org/10.1039/d0tc00985g10.1039/d0tc00985g ↗
42. Ziatdinov M, Nelson CT, Zhang X et al (2020) Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data. NPJ Comput Mater 6(1):127. https://doi.org/10.1038/s41524-020-00396-210.1038/s41524-020-00396-2 ↗
43. He J, Li J, Liu C et al (2021) Machine learning identified materials descriptors for ferroelectricity. Acta Mater 209:116815. https://doi.org/10.1016/j.actamat.2021.11681510.1016/j.actamat.2021.116815 ↗
44. Kim C, Pilania G, Ramprasad R (2016) Machine learning assisted predictions of intrinsic dielectric breakdown strength of ABX3 perovskites. J Phys Chem C 120:14575–14580. https://doi.org/10.1021/acs.jpcc.6b0506810.1021/acs.jpcc.6b05068 ↗
45. Shen Z, Bao Z, Cheng X et al (2021) Designing polymer nanocomposites with high energy density using machine learning. NPJ Comput Mater 7:110. https://doi.org/10.1038/s41524-021-00578-610.1038/s41524-021-00578-6 ↗
46. Zhai X, Chen M, Lu W (2018) Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods. Comput Mater Sci 151:41–48. https://doi.org/10.1016/j.commatsci.2018.04.03110.1016/j.commatsci.2018.04.031 ↗
47. Balachandran PV, Kowalski B, Sehirlioglu A et al (2018) Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat Commun 9(1):1668. https://doi.org/10.1038/s41467-018-03821-910.1038/s41467-018-03821-9 ↗
48. Yilmaz B, Yildirim R (2021) Critical review of machine learning applications in perovskite solar research. Nano Energy 80:105546. https://doi.org/10.1016/j.nanoen.2020.10554610.1016/j.nanoen.2020.105546 ↗
49. Geng W, Tong C, Zhang Y et al (2020) Theoretical progress on the relationship between the structures and properties of perovskite solar cells. Adv Theory Simul. https://doi.org/10.1002/adts.20200002210.1002/adts.202000022 ↗
50. He Z, Yang M, Wang L et al Concentrated photovoltaic thermoelectric hybrid system: an experimental and machine learning study. Engi Sci 15:47–56. https://doi.org/10.30919/es8d44010.30919/es8d440 ↗
51. Chen C, Xie X, Yang M et al (2021) ES Energy Environ 11:3–18. https://doi.org/10.30919/esee8c41610.30919/esee8c416 ↗
52. Jin H, Zhang H, Li J et al (2020) Discovery of Novel Two-Dimensional Photovoltaic Materials Accelerated by Machine Learning. J Phys Chem Lett 11:3075–3081. https://doi.org/10.1021/acs.jpclett.0c0072110.1021/acs.jpclett.0c00721 ↗
53. Lu S, Zhou Q, Ma L et al (2019) Rapid discovery of ferroelectric photovoltaic perovskites and material descriptors via machine learning. Small Methods 3(11). https://doi.org/10.1002/smtd.20190036010.1002/smtd.201900360 ↗
54. Srivastava M, Howard J M, Gong T et al (2021) Machine learning roadmap for perovskite photovoltaics. J Phys Chem Lett 12:7866–7877. https://doi.org/10.1021/acs.jpclett.1c0196110.1021/acs.jpclett.1c01961 ↗
55. Nelson MD, Vece MD (2019) Using a neural network to improve the optical absorption in halide perovskite layers containing core-shells silver nanoparticles. Nanomater 9(3). https://doi.org/10.3390/nano903043710.3390/nano9030437 ↗
56. Agiorgousis ML, Sun Y, Choe DH et al (2019) Machine learning augmented discovery of chalcogenide double perovskites for photovoltaics. Adv Theory Simul 2:1800173. https://doi.org/10.1002/adts.20180017310.1002/adts.201800173 ↗
57. Hartono NTP, Thapa J, Tiihonen A et al (2020) capping layers design guidelines for stable perovskite solar cells via machine learning. 47th IEEE Photovoltaic Specialists Conference:693–695. https://doi.org/10.1109/PVSC45281.2020.930062210.1109/PVSC45281.2020.9300622 ↗
58. Li J, Pradhan B, Gaur S et al (2019) Predictions and strategies learned from machine learning to develop high-performing perovskite solar cells. Adv Energy Mater 9(46). https://doi.org/10.1002/aenm.20190189110.1002/aenm.201901891 ↗
59. Takahashi K, Takahashi L, Miyazato I et al (2018) Searching for hidden perovskite materials for photovoltaic systems by combining data science and first principle calculations. ACS Photonics 5:771–775. https://doi.org/10.1021/acsphotonics.7b0147910.1021/acsphotonics.7b01479 ↗
60. Gladkikh V, Kim DY, Hajibabaei A et al (2020) Machine learning the band gaps of ABX3 Perovskites from Elemental Properties. J Phys Chem C 124:8905–8918. https://doi.org/10.1021/acs.jpcc.9b1176810.1021/acs.jpcc.9b11768 ↗
61. Li Y, Lu Y, Huo X et al (2021) Bandgap tuning strategy by cations and halide ions of lead halide perovskites learned from machine learning. RSC Adv 11:15688–15694. https://doi.org/10.1039/d1ra03117a10.1039/d1ra03117a ↗
62. Heimbrook A, Higgins K, Kalinin SV et al (2021) Exploring the physics of cesium lead halide perovskite quantum dots via Bayesian inference of the photoluminescence spectra in automated experiment. Nanophotonics 10:1977–1989. https://doi.org/10.1515/nanoph-2020-0662.10.1515/nanoph-2020-0662 ↗
63. Li Z, Xu Q, Sun Q et al (2019) Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning. Adv Funct Mater 29:1807280. https://doi.org/10.1002/adfm.20180728010.1002/adfm.201807280 ↗
64. Lu S, Zhou Q, Ouyang Y et al (2018) Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning. Nat Commun 9:3405. https://doi.org/10.1038/s41467-018-05761-w10.1038/s41467-018-05761-w ↗
65. Shao S, Xie C, Zhang L et al (2021) CsPbI3NC-sensitized SnO2/multiple-walled carbon nanotube self assembled nanomaterials with highly selective and sensitive NH3 sensing performance at room temperature. ACS Appl Mater Interfaces 13:14447–14457. https://doi.org/10.1021/acsami.0c2056610.1021/acsami.0c20566 ↗
66. Liu T, He J, Lu Z et al (2022) A visual electrochemiluminescence molecularly imprinted sensor with Ag+@UiO-66-NH2 decorated CsPbBr3 perovskite based on smartphone for point-of-care detection of nitrofurazone. Chem Eng J 429:132462. https://doi.org/10.1016/j.cej.2021.13246210.1016/j.cej.2021.132462 ↗