Skip to main content

Advertisement

SpringerLink
Log in

Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning

  • Computational Materials Design
  • Published:
Journal of Materials Science Aims and scope Submit manuscript

Abstract

The pressing need for novel materials that can serve rising demands in solar cell and optoelectronic technologies makes the nexus of halide perovskites, high-throughput computations, and machine learning, very promising. Ever increasing amounts of data on the structure, fundamental properties, and device performance of halide perovskites provide opportunities for learning chemical rules and design principles that make these materials attractive, and applying them across wide chemical spaces. In this work, we show that impurity properties of halide perovskites computed using density functional theory (DFT) can be combined with machine learning (ML) to deliver predictive models and quick identification of optoelectronically active impurity atoms. Our computation lead to the largest reported dataset of the formation energies and charge transition levels of Pb-site impurities in methylammonium lead halide (\(\hbox {MAPbX}_3\)) perovskites. Descriptors are defined to uniquely represent any impurity atom in any \(\hbox {MAPbX}_3\) compound and mapped to the computed impurity properties using regression techniques such as Gaussian process regression, neural networks, and random forests. We use the best optimized predictive models to make predictions for hundreds of impurities across 9 \(\hbox {MAPbX}_3\) compounds and create lists of dominating impurities, that is, impurities that can shift the equilibrium Fermi level in the perovskite as determined by native point defects. This accelerated screening powered by computations and machine learning can guide the identification of problematic impurities that may cause undesired recombination of charge carriers, as well as impurities that can be deliberately introduced to tune the perovskite conductivity and resulting photovoltaic absorption.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

Data and Code Availability

The entire DFT dataset, including supercell and unit cell defect calculations and all reference calculations, can be accessed from Materials Data Facility [88]. Tabulated DFT properties (impurity formation energies and charge transition levels), descriptors for ML, scripts to train and optimize neural network, random forest, Gaussian process, and linear models, and all ML predictions can be accessed here: https://github.com/mannodiarun/perovs_defects_ML.git.

References

  1. Kojima A, Teshima K, Shirai Y, Miyasaka T (2009) Organometal halide perovskites as visible-light sensitizers for photovoltaic cells. J Am Chem Soc 131:6050–6051

    Article  CAS  Google Scholar 

  2. Im J-H, Lee C-R, Lee J-W, Park S-W, Park N-G (2011) 6.5 \(\%\) efficient perovskite quantum-dot-sensitized solar cell. Nanoscale 3:4088–4093

    Article  CAS  Google Scholar 

  3. Brenner TM, Egger DA, Kronik L, Hodes G, Cahen D (2016) Hybrid organic-inorganic perovskites: low-cost semiconductors with intriguing charge-transport properties. Nat Rev Mater 1:15007

    Article  CAS  Google Scholar 

  4. Shen H et al (2018) Metal halide perovskite: a game-changer for photovoltaics and solar devices via a tandem design. Sci Technol Adv Mater 19:53–75

    Article  CAS  Google Scholar 

  5. Baikie T et al (2013) Synthesis and crystal chemistry of the hybrid perovskite (\(\text{ ch}_{{3}}\text{ nh}_{{3}}\))\(\text{ pbi}_{{3}}\) for solid-state sensitised solar cell applications. J Mater Chem A 1:5628–5641

    Article  CAS  Google Scholar 

  6. Zhou X, Jankowska J, Dong H, Prezhdo OV (2017) Recent theoretical progress in the development of perovskite photovoltaic materials. J Energy Chem 27(3):637–49

    Article  Google Scholar 

  7. Manser JS, Christians JA, Kamat PV (2016) Intriguing optoelectronic properties of metal halide perovskites. Chem Rev 116:12956–13008

    Article  CAS  Google Scholar 

  8. Qiu L, Ono LK, Qi Y (2018) Advances and challenges to the commercialization of organic-inorganic halide perovskite solar cell technology. Mater Today Energy 7:169–189

    Article  Google Scholar 

  9. Yin W-J, Yang J-H, Kang J, Yan Y, Wei S-H (2015) Halide perovskite materials for solar cells: a theoretical review. J Mater Chem A 3:8926–8942

    Article  CAS  Google Scholar 

  10. Yan Y, Yin W-J, Shi T, Meng W, Feng C (2016) Defect Physics of \(\text{ CH}_{{3}}\text{ NH}_{{3}}\)PbX3 (X = I, Br, Cl) Perovskites, 79–105. Springer International Publishing, Cham

    Google Scholar 

  11. López CA et al (2017) Elucidating the methylammonium (ma) conformation in \(\text{ mapbbr}_{{3}}\) perovskite with application in solar cells. Inorg Chem 56:14214–14219

    Article  Google Scholar 

  12. (NREL), N. R. E. L. National renewable energy laboratory (nrel) efficiency chart (2016)

  13. Muhammad Z et al (2020) Tunable relativistic quasiparticle electronic and excitonic behavior of the fapb(i1-xbrx)3 alloy. Phys Chem Chem Phys 22:11943–11955

    Article  CAS  Google Scholar 

  14. Chang J et al (2019) Electronic and optical properties of perovskite compounds (ma, fa)pb(i, x)\(_{3}\) (x = cl, br) explored for photovoltaic applications. RSC Adv 9:7015–7024

    Article  CAS  Google Scholar 

  15. Banerjee A, Chakraborty S, Ahuja R (2019) Rashba triggered electronic and optical properties tuning in mixed cation-mixed halide hybrid perovskites. ACS Appli Energy Mater 2:6990–6997

    Article  CAS  Google Scholar 

  16. Hao F, Stoumpos CC, Chang RPH, Kanatzidis MG (2014) Anomalous band gap behavior in mixed sn and pb perovskites enables broadening of absorption spectrum in solar cells. J Am Chem Soc 136:8094–8099

    Article  CAS  Google Scholar 

  17. Wu M-J et al (2019) Bandgap engineering enhances the performance of mixed-cation perovskite materials for indoor photovoltaic applications. Adv Energy Mater 9:1901863

    Article  CAS  Google Scholar 

  18. Khatun S, Maiti A, Pal AJ (2020) Bowing of transport gap in hybrid halide perovskite alloys (ch3nh3sn1-xpbxi3): Which band is responsible? Appl Phys Lett 116:0012104

    Article  CAS  Google Scholar 

  19. Park JS, Kim S, Xie Z, Walsh A (2018) Point defect engineering in thin-film solar cells. Nat Rev Mater 3:194–210

    Article  CAS  Google Scholar 

  20. Ding J et al (2019) Cesium decreases defect density and enhances optoelectronic properties of mixed ma1-xcsxpbbr3 single crystal. J Phys Chem C 123:14969–14975

    Article  CAS  Google Scholar 

  21. Subedi B et al (2020) Effects of intrinsic and atmospherically induced defects in narrow bandgap (fasni3)x(mapbi3)1–x perovskite films and solar cells. J Chem Phys 152:064705

    Article  CAS  Google Scholar 

  22. Mannodi-Kanakkithodi A et al (2019) Comprehensive computational study of partial lead substitution in methylammonium lead bromide. Chem Mater 31:3599–3612

    Article  CAS  Google Scholar 

  23. Mannodi-Kanakkithodi A, Park J-S, Martinson ABF, Chan MKY (2020) Defect energetics in pseudo-cubic mixed halide lead perovskites from first-principles. J Phys Chem C 124:16729–16738

    Article  CAS  Google Scholar 

  24. Whitfield PS et al (2016) Structures, phase transitions and tricritical behavior of the hybrid perovskite methyl ammonium lead iodide. Sci Rep 6:35685

    Article  CAS  Google Scholar 

  25. Bechtel JS, Van der Ven A (2018) First-principles thermodynamics study of phase stability in inorganic halide perovskite solid solutions. Phys Rev Mater 2:045401

    Article  CAS  Google Scholar 

  26. Greenland C et al (2020) Correlating phase behavior with photophysical properties in mixed-cation mixed-halide perovskite thin films. Adv Energy Mater 10:1901350

    Article  CAS  Google Scholar 

  27. Beal RE et al (2020) Structural origins of light-induced phase segregation in organic-inorganic halide perovskite photovoltaic materials. Matter 2:207–219

    Article  Google Scholar 

  28. Kim S-Y et al (2019) Ternary diagrams of the phase, optical bandgap energy and photoluminescence of mixed-halide perovskites. Acta Mater 181:460–469

    Article  CAS  Google Scholar 

  29. Chu Z et al (2021) Emerging low-dimensional crystal structure of metal halide perovskite optoelectronic materials and devices. Small Struct 2:2000133

    Article  CAS  Google Scholar 

  30. Liu P, Yu S, Xiao S (2021) Research progress on two-dimensional (2d) halide organic-inorganic hybrid perovskites. Sustain Energy Fuels 5:3950–3978

    Article  CAS  Google Scholar 

  31. Penev ES, Marzari N, Yakobson BI (2021) Theoretical prediction of two-dimensional materials, behavior, and properties. ACS Nano 15:5959–5976

    Article  CAS  Google Scholar 

  32. Long G et al (2020) Chiral-perovskite optoelectronics. Nat Rev Mater 5:423–439

    Article  Google Scholar 

  33. Sadoughi G et al (2015) Observation and mediation of the presence of metallic lead in organic-inorganic perovskite films. ACS Appli Mater Interfaces 7:13440–13444

    Article  CAS  Google Scholar 

  34. Zhang X, Turiansky ME, Van de Walle CG (2020) Correctly assessing defect tolerance in halide perovskites. J Phys Chem C 124:6022–6027

    Article  CAS  Google Scholar 

  35. Zhang X, Turiansky ME, Shen J-X, Van de Walle CG (2020) Iodine interstitials as a cause of nonradiative recombination in hybrid perovskites. Phys Rev B 101:140101

    Article  CAS  Google Scholar 

  36. Park J-S, Walsh A (2021) Modeling grain boundaries in polycrystalline halide perovskite solar cells. Annual Review of Condensed Matter Physics 12:95–109

    Article  CAS  Google Scholar 

  37. Long R, Liu J, Prezhdo OV (2016) Unravelling the effects of grain boundary and chemical doping on electron-hole recombination in ch3nh3pbi3 perovskite by time-domain atomistic simulation. J Am Chem Soc 138:3884–3890

    Article  CAS  Google Scholar 

  38. Yin W-J, Shi T, Yan Y (2014) Unusual defect physics in \(\text{ ch}_{{3}}\text{ nh}_{{3}}\text{ pbi}_{{3}}\) perovskite solar cell absorber. Appl Phys Lett 104:063903

    Article  CAS  Google Scholar 

  39. Shi T, Yin W-J, Hong F, Zhu K, Yan Y (2015) Unipolar self-doping behavior in perovskite \(\text{ ch}_{{3}}\text{ nh}_{{3}}\text{ pbbr}_{{3}}\). Appl Phys Lett 106:103902

    Article  CAS  Google Scholar 

  40. Sampson MD, Park JS, Schaller RD, Chan MKY, Martinson ABF (2017) Transition metal-substituted lead halide perovskite absorbers. J Mater Chem A 5:3578–3588

    Article  CAS  Google Scholar 

  41. Cao DH et al (2019) Charge transfer dynamics of phase-segregated halide perovskites: \(\text{ Ch}_{{3}}\text{ nh}_{{3}}\text{ pbcl}_{{3}}\) and \(\text{ ch}_{{3}}\text{ nh}_{{3}}\text{ pbi}_{{3}}\) or (\(\text{ c4h9nh}_{{3}}\))2(\(\text{ ch}_{{3}}\text{ nh}_{{3}}\))n-1pbni3n+1 mixtures. ACS Appli Mater Interfaces 11:9583–9593

    Article  CAS  Google Scholar 

  42. Mannodi-Kanakkithodi A et al (2020) Machine-learned impurity level prediction for semiconductors: the example of cd-based chalcogenides. npj Comput Mater 6(1):1–4

    Google Scholar 

  43. Freysoldt C, Neugebauer J, Van de Walle CG (2009) Fully ab initio finite-size corrections for charged-defect supercell calculations. Phys Rev Lett 102:016402

    Article  CAS  Google Scholar 

  44. Freysoldt C et al (2014) First-principles calculations for point defects in solids. Rev Mod Phys 86:253–305

    Article  Google Scholar 

  45. Schmidt J, Marques MRG, Botti S, Marques MAL (2019) Recent advances and applications of machine learning in solid-state materials science. npj Comput Mater 5(1):1–36. https://doi.org/10.1038/s41524-019-0221-0

    Article  Google Scholar 

  46. Vasudevan RK et al (2019) Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics. MRS Communications 9:821–838

    Article  CAS  Google Scholar 

  47. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C (2017) Machine learning in materials informatics: recent applications and prospects. npj Comput Mater 3(1):54

    Article  Google Scholar 

  48. Mannodi-Kanakkithodi A et al (2018) Scoping the polymer genome: a roadmap for rational polymer dielectrics design and beyond. Mater Today 21:785–796

    Article  CAS  Google Scholar 

  49. Deringer VL, Csányi G (2017) Machine learning based interatomic potential for amorphous carbon. Phys Rev B 95:094203

    Article  Google Scholar 

  50. Botu V, Batra R, Chapman J, Ramprasad R (2017) Machine learning force fields: construction, validation, and outlook. J Phys Chem C 121:511–522

    Article  CAS  Google Scholar 

  51. Pilania G, Gubernatis JE, Lookman T (2015) Structure classification and melting temperature prediction in octet ab solids via machine learning. Phys Rev B 91:214302

    Article  CAS  Google Scholar 

  52. Oliynyk AO, Adutwum LA, Harynuk JJ, Mar A (2016) Classifying crystal structures of binary compounds ab through cluster resolution feature selection and support vector machine analysis. Chem Mater 28:6672–6681

    Article  CAS  Google Scholar 

  53. Medasani B et al (2016) Predicting defect behavior in b2 intermetallics by merging ab initio modeling and machine learning. npj Comput Mater 2(1):1

    Article  CAS  Google Scholar 

  54. Mannodi-Kanakkithodi A et al (2022) Universal machine learning framework for defect predictions in zinc blende semiconductors. Patterns. https://doi.org/10.1016/j.patter.2022.100450

    Article  Google Scholar 

  55. Mannodi-Kanakkithodi A, Chan MK (2021) Computational data-driven materials discovery. Trends Chem 3:79–82

    Article  CAS  Google Scholar 

  56. Yamada H et al (2019) Predicting materials properties with little data using shotgun transfer learning. ACS Cent Sci 5:1717–1730

    Article  CAS  Google Scholar 

  57. Kresse G, Hafner J (1994) Ab initio molecular-dynamics simulation of the liquid-metal-amorphous-semiconductor transition in germanium. Phys Rev B 49:14251–14269

    Article  CAS  Google Scholar 

  58. Kresse G, Furthmüller J (1996) Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B 54:11169–11186

    Article  CAS  Google Scholar 

  59. Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77:3865–3868

    Article  CAS  Google Scholar 

  60. Blöchl PE (1994) Projector augmented-wave method. Phys Rev B 50:17953–17979

    Article  Google Scholar 

  61. Wei S-H, Ferreira LG, Bernard JE, Zunger A (1990) Electronic properties of random alloys: special quasirandom structures. Phys Rev B 42:9622–9649

    Article  CAS  Google Scholar 

  62. Jiang Z et al (2016) Special quasirandom structures for perovskite solid solutions. J Phys: Condens Matter 28:475901

    Google Scholar 

  63. Whalley LD, Frost JM, Jung Y-K, Walsh A (2017) Perspective: theory and simulation of hybrid halide perovskites. J Chem Phys 146:220901

    Article  CAS  Google Scholar 

  64. Chan M, Ceder G (2010) Efficient band gap prediction for solids. Phys Rev Lett 105:196403

    Article  CAS  Google Scholar 

  65. Heyd J, Peralta JE, Scuseria GE, Martin RL (2005) Energy band gaps and lattice parameters evaluated with the heyd-scuseria-ernzerhof screened hybrid functional. J Chem Phys 123:174101

    Article  CAS  Google Scholar 

  66. Aryasetiawan F, Gunnarsson O (1998) TheGWmethod. Rep Prog Phys 61:237–312

    Article  CAS  Google Scholar 

  67. Sun R, Chan MKY, Kang S, Ceder G (2011) Intrinsic stoichiometry and oxygen-induced \(p\)-type conductivity of pyrite fes\({}_{2}\). Phys Rev B 84:035212

    Article  CAS  Google Scholar 

  68. Yang Y et al (2018) Effect of doping of nai monovalent cation halide on the structural, morphological, optical and optoelectronic properties of mapbi3 perovskite. J Mater Sci 29:205–210

    CAS  Google Scholar 

  69. Park IJ et al (2017) Effect of rubidium incorporation on the structural, electrical, and photovoltaic properties of methylammonium lead iodide-based perovskite solar cells. ACS Appl Mater Interface 9:41898–41905

    Article  CAS  Google Scholar 

  70. Tang Z et al (2017) Hysteresis-free perovskite solar cells made of potassium-doped organometal halide perovskite. Sci Rep 7:12183

    Article  CAS  Google Scholar 

  71. Wu M-C, Lin T-H, Chan S-H, Su W-F (2017) Improved efficiency of perovskite photovoltaics based on ca-doped methylammonium lead halide. J Taiwan Inst Chem Eng 80:695–700

    Article  CAS  Google Scholar 

  72. Kye Y-H, Yu C-J, Kim C-H, Kim Y-S, Jong U-G (2021) Influence of metal-ion replacement on the phase stabilization of cubic all-inorganic cesium lead halide perovskites: an ab initio thermodynamic formalism for solution-processed cation doping. J Phys Chem C 125:13195–13211

    Article  CAS  Google Scholar 

  73. Mayengbam R, Tripathy S, Palai G (2020) Structural, electronic, optical and mechanical properties of zn-doped mapbi3 perovskites and absorber layer efficiencies: An ab-initio investigation. Mater Today Commun 24:101216

    Article  CAS  Google Scholar 

  74. Elton DC, Boukouvalas Z, Butrico MS, Fuge MD, Chung PW, (2018) Applying machine learning techniques to predict the properties of energetic materials. Sci Reports 8:9059

  75. Ouyang R, Curtarolo S, Ahmetcik E, Scheffler M, Ghiringhelli LM (2018) Sisso: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys Rev Mater 2:083802

    Article  CAS  Google Scholar 

  76. Fan J, Lv J (2008) Sure independence screening for ultrahigh dimensional feature space. J Royal Statis Soc: Series B (Statistical Methodology) 70:849–911

    Article  Google Scholar 

  77. Dolgirev PE, Kruglov IA, Oganov AR (2016) Machine learning scheme for fast extraction of chemically interpretable interatomic potentials. AIP Adv 6:085318

    Article  CAS  Google Scholar 

  78. Marques MRG, Wolff J, Steigemann C, Marques MAL (2019) Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures. Phys Chem Chem Phys 21:6506–6516

    Article  CAS  Google Scholar 

  79. Mannodi-Kanakkithodi A, Chan MKY (2021) Data-driven design of novel halide perovskite alloys 2109:10798

  80. Muller B, Strickland MT, Reinhardt J Neural Networks (Springer-Verlag, 1995)

  81. Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In 2013 IEEE International conference on acoustics, speech and signal processing, 8599–8603 (Vancouver, BC, Canada, )

  82. Diez M Porosity Optimization in Nanoporous materials via Machine Learning. Master’s thesis, Polytechnic University of Turin

  83. Breiman L (2001) Random foresets. Mach Learn 45:5–32

    Article  Google Scholar 

  84. Varoquaux G et al (2015) Scikit-learn: Machine learning without learning the machinery. GetMobile: Mobile Comp Comm 19:29–33

    Article  Google Scholar 

  85. Williams CK, Rasmussen CE (2006) Gaussian processes for machine learning, vol 2. MIT press Cambridge, MA

    Google Scholar 

  86. Seeger M, (2004) Gaussian processes for machine learning. Int J Neural Syst 11:69–106

  87. Puga JL, Krzywinski M, Altman N (2015) Bayes’ theorem 12:277–278

  88. Mannodi-Kanakkithodi A, Chan MK (2022) High-throughput density functional theory dataset of pb-site impurities in hybrid perovskites . https://petreldata.net/mdf/detail/mapbx3$_$defect$_$v1.1

Download references

Acknowledgements

Extensive discussions with and scientific feedback from Dr. Alex Martinson (Argonne), Dr. David Fenning (UCSD), Rishi Kumar (UCSD), and Dr. Ji-Sang Park (Kyungpook National University) are acknowledged. This work was performed, partly at the Center for Nanoscale Materials, a U.S. Department of Energy Office of Science User Facility, and supported by the U.S. Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357, and partly at Purdue University, under startup account number F.10023800.05.002. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. We gratefully acknowledge the computing resources provided on Bebop, a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory.

Author information

Authors and Affiliations

  1. School of Materials Engineering, Purdue University, West Lafayette, Indiana, 47906, USA

    Arun Mannodi-Kanakkithodi

  2. Center for Nanoscale Materials, Argonne National Laboratory, Argonne, Illinois, 60439, USA

    Arun Mannodi-Kanakkithodi & Maria K. Y. Chan

Authors
  1. Arun Mannodi-Kanakkithodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria K. Y. Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arun Mannodi-Kanakkithodi.

Ethics declarations

Conflict of Interest

The authors declare no conflict of interest.

Additional information

Handling Editor: M. Grant Norton.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 2207 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mannodi-Kanakkithodi, A., Chan, M.K.Y. Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning. J Mater Sci 57, 10736–10754 (2022). https://doi.org/10.1007/s10853-022-06998-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10853-022-06998-z

Search

Navigation