Identifying Pb-free perovskites for solar cells by machine learning
Source abstract
Abstract
Abstract Recent advances in computing power have enabled the generation of large datasets for materials, enabling data-driven approaches to problem-solving in materials science, including materials discovery. Machine learning is a primary tool for manipulating such large datasets, predicting unknown material properties and uncovering relationships between structure and property. Among state-of-the-art machine learning algorithms, gradient-boosted regression trees (GBRT) are known to provide highly accurate predictions, as well as interpretable analysis based on the importance of features. Here, in a search for lead-free perovskites for use in solar cells, we applied the GBRT algorithm to a dataset of electronic structures for candidate halide double perovskites to predict heat of formation and bandgap. Statistical analysis of the selected features identifies design guidelines for the discovery of new lead-free perovskites.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Perovskite Materials and Applications · Quantum Dots Synthesis And Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (53)
Show source-provided references
1. Fischer, C. C. et al. Predicting crystal structure by merging data mining with quantum mechanics. Nat. Mater. 5, 641 (2006).10.1038/nmat1691 ↗
2. Armiento, R. et al. Screening for high-performance piezoelectrics using high-throughput density functional theory. Phys. Rev. B 84, 014103 (2011).10.1103/PhysRevB.84.014103 ↗
3. Hautier, G. et al. Identification and design principles of low hole effective mass p-type transparent conducting oxides. Nat. Commun. 4, 2292 (2013).10.1038/ncomms3292 ↗
4. Carrete, J. et al. Nanograined half-heusler semiconductors as advanced thermoelectrics: An ab initio high-throughput statistical study. Adv. Funct. Maters. 24, 7427 (2014).10.1002/adfm.201401201 ↗
5. Yim, K. et al. Novel high-κ dielectrics for next-generation electronic devices screened by automated ab initio calculations. NPG Asia Mater. 7, e190 (2015).10.1038/am.2015.57 ↗
6. Agrawala, 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 ↗
7. Jain, A. et al. New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships. J. Mater. Res. 31, 977 (2016).10.1557/jmr.2016.80 ↗
8. Kalidindi, S. et al. Role of materials data science and informatics in accelerated materials innovation. Mrs. Bull. 41, 596 (2016).10.1557/mrs.2016.164 ↗
9. Boyd, P. G., Lee, Y. & Smit, B. Computational development of the nanoporous materials genome. Nat. Rev. Mater. 2, 17037 (2017).10.1038/natrevmats.2017.37 ↗
10. Jain, A. et al. The materials project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).10.1063/1.4812323 ↗
11. Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials design and discovery with high-throughput density functional theory: The Open Quantum Materials Database (OQMD). JOM 65, 1501 (2013).10.1007/s11837-013-0755-4 ↗
12. Kirklin, S. et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies. npj Comput. Mater. 1, 15010 (2015).10.1038/npjcompumats.2015.10 ↗
13. Draxl, C. & Scheffler, M. NOMAD: The FAIR concept for big data-driven materials science. Mrs. Bull. 43, 676 (2018).10.1557/mrs.2018.208 ↗
14. Curtarolo, S. et al. AFLOW: An automatic framework for high-throughput materials discovery. Comp. Mater. Sci. 58, 218 (2012).10.1016/j.commatsci.2012.02.005 ↗
15. Carr, D. A. et al. Machine learning approach for structure-based zeolite classification. Micro Meso. Mater. 117, 339 (2009).10.1016/j.micromeso.2008.07.027 ↗
16. Pilania, G., Gubernatis, J. E. & Lookman, T. Structure classification and melting temperature prediction in octet AB solids via machine learning. Phys. Rev. B 91, 214302 (2015).10.1103/PhysRevB.91.214302 ↗
17. Yamashita, T. et al. Crystal structure prediction accelerated by Bayesian optimization. Phys. Rev. Mater. 2, 013803 (2018).10.1103/PhysRevMaterials.2.013803 ↗
18. Hobday, S. et al. Applications of neural networks to fitting interatomic potential functions. Model. Simul. Mater. Sci. Eng. 7, 397 (1999).10.1088/0965-0393/7/3/308 ↗
19. Handkey, C. M. & Popelier, L. A. Potential energy surfaces fitted by artificial neural networks. J. Phys. Chem. A 114, 3371 (2010).10.1021/jp9105585 ↗
20. Schneider, E. et al. Stochastic neural network approach for learning high-dimensional free energy surfaces. Phys. Rev. Lett. 119, 150601 (2017).10.1103/PhysRevLett.119.150601 ↗
21. Wellendorff, J. et al. Density functionals for surface science: Exchange-correlation model development with Bayesian error estimation. Phys. Rev. B 85, 235149 (2012).10.1103/PhysRevB.85.235149 ↗
22. Snyder, J. C. et al. Finding density functionals with machine learning. Phys. Rev. Lett. 108, 253002 (2012).10.1103/PhysRevLett.108.253002 ↗
23. Brockherde, F. et al. Bypassing the Kohn-Sham equations with machine learning. Nat. Commun. 8, 872 (2017).10.1038/s41467-017-00839-3 ↗
24. Pilania, G. et al. Accelerating materials property predictions using machine learning. Sci. Rep. 3, 2810 (2013).10.1038/srep02810 ↗
25. Lee, J. et al. Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques. Phys. Rev. B 93, 115104 (2016).10.1103/PhysRevB.93.115104 ↗
26. Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, 19375 (2016).10.1038/srep19375 ↗
27. Bartok, A. P. et al. Machine learning unifies the modeling of materials and molecules. Sci. Adv. 3, e170816 (2017).10.1126/sciadv.1701816 ↗
28. Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. arXiv:1603.02754 (2016).10.1145/2939672.2939785 ↗
29. Chen, W. et al. Efficient and stable large-area perovskite solar cells with inorganic charge extraction layers. Science 350, 944–948 (2015).10.1126/science.aad1015 ↗
30. Shin, S. et al. Colloidally prepared La-doped BaSnO3 electrodes for efficient, photostable perovskite solar cells. Science 356, 167–171 (2017).10.1126/science.aam6620 ↗
31. Volonakis, G. et al. Lead-free halide double perovskites via heterovalent substitution of noble metals. J. Phys. Chem. Lett. 7, 1254 (2016).10.1021/acs.jpclett.6b00376 ↗
32. Philip, M. R. et al. Band gaps of the lead-free halide double perovskites Cs2BiAgCl6 and Cs2BiAgBr6 from theory and experiment. J. Phys. Chem. Lett. 7, 2579 (2016).10.1021/acs.jpclett.6b01041 ↗
33. Wei, F. et al. Synthesis and properties of a lead-free hybrid double perovskite: (CH3NH3)2AgBiBr6. Chem. Mater. 29, 1089 (2017).10.1021/acs.chemmater.6b03944 ↗
34. Volonakis, G. et al. Cs2InAgCl6: A new lead-free halide double perovskite with direct band gap. J. Phys. Chem. Lett. 8, 772 (2017).10.1021/acs.jpclett.6b02682 ↗
35. Shockley, W. & Queisser, H. J. Detailed balance limit of efficiency of p-n junction solar cells. J. Appl. Phys. 32, 510–519 (1961).10.1063/1.1736034 ↗
36. Jin, H., Im, J. & Freeman, A. J. Topological insulator phase in halide perovskite structure. Phys. Rev. B 86, 121102 (2012).10.1103/PhysRevB.86.121102 ↗
37. Menéndez-Proupin, E., Palacios, P., Wahnón, P. & Conesa, J. C. Self-consistent relativistic band structure of the CH3NH3PbI3 perovskite. Phys. Rev. B 90, 045207 (2014).10.1103/PhysRevB.90.045207 ↗
38. Umari, P., Moscon, E. & Angelis, F. D. Relativistic GW calculations on CH3NH3PbI3 and CH3NH3SnI3 perovskites for solar cell applications. Sci. Report. 4, 4467 (2014).10.1038/srep04467 ↗
39. Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. Classification and Regression Trees (The Wadsworth statistics/probability series). (Chapman and Hall, New York, 1984).
40. Strobl, C., Malley, J. & Tutz, G. An introduction to recursive partitioning: rationale, application and characteristics of classification and regression trees, bagging and random forests. Psycol. Methods 14, 323 (2009).10.1037/a0016973 ↗
41. Breiman, L. Bagging predictors. Mach. Learn. 24, 123 (1996).
42. Ho, T. K. The random subspace method for constructing decision forests. IEEE T. Pattern Anal. 20, 832 (1998).10.1109/34.709601 ↗
43. Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189 (2001).10.1214/aos/1013203451 ↗
44. Hastie, R. & Tibshirani, J. F. The elements of statistical learning: Data mining, inference, and prediction (Springer Series in Statistics, Springer, 2001).10.1007/978-0-387-21606-5 ↗
45. Hautier, G. et al. Accuracy of density functional theory in predicting formation energies of ternary oxides from binary oxides and its implication on phase stability. Phys. Rev. B 85, 155208 (2012).10.1103/PhysRevB.85.155208 ↗
46. Schutt, K. T. et al. How to represent crystal structures for machine learning: Towards fast prediction of electronic properties. Phys. Rev. B 89, 205118 (2014).10.1103/PhysRevB.89.205118 ↗
47. Ghiringhelli, L. M. et al. Big data of materials science: Critical role of the descriptor. Phys. Rev. Lett. 114, 105503 (2015).10.1103/PhysRevLett.114.105503 ↗
48. Stoumpos, C. C., Malliakas, C. D. & Kanatzidis, M. G. Semiconducting tin and lead iodide perovskites with organic cations: Phase transitions, high mobilities, and near-infrared photoluminescent properties. Inorg. Chem. 52, 9019 (2013).10.1021/ic401215x ↗
49. Blöchl, P. E. Phys. Rev. B 50, 17953 (1994).10.1103/PhysRevB.50.17953 ↗
50. Kresse, G. & Furthmüller Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. J. Comput. Mat. Sci. 6, 15 (1996).10.1016/0927-0256(96)00008-0 ↗
51. Kresse, G. & Furthmüller Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. J. Phys. Rev. B 54, 11169 (1996).10.1103/PhysRevB.54.11169 ↗
52. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865 (1996).10.1103/PhysRevLett.77.3865 ↗
53. Castelli, I. E., Thygesen, K. S. & Jacobsen, K. W. Bandgap engineering of double perovskites for one- and two-photon water splitting. Mater. Res. Soc. Symp. Proc. 1523, https://doi.org/10.1557/opl.2013.450 (2013).10.1557/opl.2013.450 ↗