{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T08:11:24Z","timestamp":1793779884402,"version":"4.6.1"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Innovation Capability Support Plan Project of Shaanxi Province","award":["2024ZC-KJXX-020"],"award-info":[{"award-number":["2024ZC-KJXX-020"]}]},{"name":"the Innovation Capability Support Plan Project of Shaanxi Province","award":["20230520"],"award-info":[{"award-number":["20230520"]}]},{"name":"the Innovation Capability Support Plan Project of Shaanxi Province","award":["2024JC-YBQN-0567"],"award-info":[{"award-number":["2024JC-YBQN-0567"]}]},{"name":"the Shaanxi Association for Science and Technology Youth Talent Support Program","award":["2024ZC-KJXX-020"],"award-info":[{"award-number":["2024ZC-KJXX-020"]}]},{"name":"the Shaanxi Association for Science and Technology Youth Talent Support Program","award":["20230520"],"award-info":[{"award-number":["20230520"]}]},{"name":"the Shaanxi Association for Science and Technology Youth Talent Support Program","award":["2024JC-YBQN-0567"],"award-info":[{"award-number":["2024JC-YBQN-0567"]}]},{"name":"the General Projects of Shaanxi Provincial Department of Science and Technology","award":["2024ZC-KJXX-020"],"award-info":[{"award-number":["2024ZC-KJXX-020"]}]},{"name":"the General Projects of Shaanxi Provincial Department of Science and Technology","award":["20230520"],"award-info":[{"award-number":["20230520"]}]},{"name":"the General Projects of Shaanxi Provincial Department of Science and Technology","award":["2024JC-YBQN-0567"],"award-info":[{"award-number":["2024JC-YBQN-0567"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>The search for stable, lead-free perovskite materials is critical for developing efficient and environmentally friendly energy solutions. In this study, machine learning methods were applied to predict the bandgap and formation energy of double perovskites, aiming to identify promising photovoltaic candidates. A dataset of 1053 double perovskites was extracted from the Materials Project database, with 50 feature descriptors generated. Feature selection was carried out using Pearson correlation and mRMR methods, and 23 key features for bandgap prediction and 18 key features for formation energy prediction were determined. Four algorithms, including gradient-boosting regression (GBR), random forest regression (RFR), LightGBM, and XGBoost, were evaluated, with XGBoost demonstrating the best performance (R2 = 0.934 for bandgap, R2 = 0.959 for formation energy; MAE = 0.211 eV and 0.013 eV\/atom). The SHAP (Shapley Additive Explanations) analysis revealed that the X-site electron affinity positively influences the bandgap, while the B\u2033-site first and third ionization energies exhibit strong negative effects. Formation energy is primarily governed by the X-site first ionization energy and the electronegativities of the B\u2032 and B\u2033 sites. To identify optimal photovoltaic materials, 4573 charge-neutral double perovskites were generated via elemental substitution, with 2054 structurally stable candidates selected using tolerance and octahedral factors. The XGBoost model predicted bandgaps, yielding 99 lead-free double perovskites with ideal bandgaps (1.3~1.4 eV). Among them, four candidates are known compounds according to the Materials Project database, namely Ca2NbFeO6, Ca2FeTaO6, La2CrFeO6, and Cs2YAgBr6, while the remaining 95 candidate perovskites are unknown compounds. Notably, X-site elements (Se, S, O, C) and B\u2033-site elements (Pd, Ir, Fe, Ta, Pt, Cu) favor narrow bandgap formation. These findings provide valuable guidance for designing high-performance, non-toxic photovoltaic materials.<\/jats:p>","DOI":"10.3390\/molecules30112378","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T04:27:58Z","timestamp":1748579278000},"page":"2378","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning"],"prefix":"10.3390","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2085-3727","authenticated-orcid":false,"given":"Juan","family":"Wang","sequence":"first","affiliation":[{"name":"Xi\u2019an Key Laboratory of Advanced Photo-Electronics Materials and Energy Conversion Device, School of Electronic Information, Xijing University, Xi\u2019an 710123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhe","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Key Laboratory of Advanced Photo-Electronics Materials and Energy Conversion Device, School of Electronic Information, Xijing University, Xi\u2019an 710123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqin","family":"Liu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Key Laboratory of Advanced Photo-Electronics Materials and Energy Conversion Device, School of Electronic Information, Xijing University, Xi\u2019an 710123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzhong","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Key Laboratory of Advanced Photo-Electronics Materials and Energy Conversion Device, School of Electronic Information, Xijing University, Xi\u2019an 710123, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1126\/science.1243982","article-title":"Electron-hole diffusion lengths exceeding l micrometer in an organometal trihalide perovskite absorber","volume":"342","author":"Stranks","year":"2013","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109339","DOI":"10.1016\/j.nanoen.2024.109339","article-title":"Designing high-performance pure-red metal halide perovskite materials and light-emitting diodes for Rec. 2020 display","volume":"122","author":"Mei","year":"2024","journal-title":"Nano Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2230","DOI":"10.1021\/acs.chemrev.0c01006","article-title":"The 2D halide perovskite rulebook: How the spacer influences everything from the structure to optoelectronic device efficiency","volume":"121","author":"Li","year":"2021","journal-title":"Chem. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12287","DOI":"10.1039\/D4CC04000G","article-title":"Toward durable all-inorganic perovskite solar cells: From lead-based to lead-free","volume":"60","author":"Xu","year":"2024","journal-title":"Chem. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1021\/acs.chemmater.5b04231","article-title":"Cs2AgBiX6 (X = Br, Cl): New visible light absorbing, lead-free halide perovskite semiconductors","volume":"28","author":"McClure","year":"2016","journal-title":"Chem. Mater."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1021\/jacs.5b13294","article-title":"A bismuth-halide double perovskite with long carrier recombination lifetime for photovoltaic applications","volume":"138","author":"Slavney","year":"2016","journal-title":"J. Am. Chem. Soc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Min, H., Hong, S., Song, J., Son, B., Noh, B., and Moon, J. (2024). SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea. Electronics, 13.","DOI":"10.3390\/electronics13112071"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"114479","DOI":"10.1016\/j.rser.2024.114479","article-title":"Multi-step photovoltaic power forecasting using transformer and recurrent neural networks","volume":"200","author":"Kim","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"132089","DOI":"10.1016\/j.seppur.2025.132089","article-title":"Prediction of perfluorooctanoic acid adsorption properties of porous carbon materials based on machine learning","volume":"363","author":"Jiang","year":"2025","journal-title":"Sep. Purif. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.jechem.2024.09.043","article-title":"Comprehensive review of advances in machine-learning-driven optimization and characterization of perovskite materials for photovoltaic devices","volume":"101","author":"Jo","year":"2025","journal-title":"J. Energy Chem."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8926","DOI":"10.1039\/C4TA05033A","article-title":"Halide perovskite materials for solar cells: A theoretical review","volume":"3","author":"Yin","year":"2015","journal-title":"J. Mater. Chem. A"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.1038\/s41467-018-05761-w","article-title":"Accelerated discovery of stable lead-free hybrid organic inorganic perovskites via machine learning","volume":"9","author":"Lu","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112354","DOI":"10.1016\/j.mtcomm.2025.112354","article-title":"Accelerating the discovery of direct bandgap perovskites for solar energy via machine learning","volume":"45","author":"Zhan","year":"2025","journal-title":"Mater. Today Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6385","DOI":"10.1039\/D4RA00402G","article-title":"Predicting band gaps of ABN3 perovskites: An account from machine learning and first-principle DFT studies","volume":"14","author":"Ghosh","year":"2024","journal-title":"RSC Adv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112209","DOI":"10.1016\/j.solener.2023.112209","article-title":"Machine learning-aided discovery of bismuth-based transition metal oxide double perovskites for solar cell applications","volume":"267","author":"Sradhasagar","year":"2024","journal-title":"Sol. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.solener.2021.09.030","article-title":"Machine learning stability and band gap of lead-free halide double perovskite materials for perovskite solar cells","volume":"228","author":"Guo","year":"2021","journal-title":"Sol. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"150916","DOI":"10.1016\/j.apsusc.2021.150916","article-title":"Screening for lead-free inorganic double perovskites with suitable band gaps and high stability using combined machine learning and DFT calculation","volume":"568","author":"Gao","year":"2021","journal-title":"Appl. Surf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1139\/cjc-2023-0156","article-title":"Predicting the band gap of lead-free inorganic double perovskites using modified parallel residual network","volume":"102","author":"Luo","year":"2024","journal-title":"Can. J. Chem."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105841","DOI":"10.1016\/j.mtcomm.2023.105841","article-title":"High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap","volume":"35","author":"Hu","year":"2023","journal-title":"Mater. Today Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111476","DOI":"10.1016\/j.commatsci.2022.111476","article-title":"Discovery of direct band gap perovskites for light harvesting by using machine learning","volume":"210","author":"Rath","year":"2022","journal-title":"Comput. Mater. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1360\/SST-2023-0104","article-title":"Machine-learning-based prediction of cubic perovskite formation energy and magnetism","volume":"54","author":"Chen","year":"2024","journal-title":"Sci. Sin. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.matt.2020.04.016","article-title":"Crystal Site Feature Embedding Enables Exploration of Large Chemical Spaces","volume":"3","author":"Choubisa","year":"2020","journal-title":"Matter"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"10073","DOI":"10.1021\/acs.chemrev.1c00022","article-title":"Gaussian Process Regression for Materials and Molecules","volume":"121","author":"Deringer","year":"2021","journal-title":"Chem. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112889","DOI":"10.1016\/j.commatsci.2024.112889","article-title":"Integrative approach of machine learning and symbolic regression for stability prediction of multicomponent perovskite oxides and high-throughput screening","volume":"236","author":"Zhang","year":"2024","journal-title":"Comput. Mater. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"024503","DOI":"10.1063\/5.0013136","article-title":"Modeling the dielectric constants of crystals using machine learning","volume":"153","author":"Morita","year":"2020","journal-title":"J. Chem. Phys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1039\/D0EE03839C","article-title":"22.8%-Efficient single-crystal mixed-cation inverted perovskite solar cells with a near-optimal bandgap","volume":"14","author":"Alsalloum","year":"2021","journal-title":"Energy Environ. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100320","DOI":"10.1016\/j.rio.2022.100320","article-title":"The Shockley\u2013Queisser limit and the conversion efficiency of silicon-based solar cells","volume":"9","author":"Zanatta","year":"2022","journal-title":"Results Opt."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/01605682.2021.1919035","article-title":"Gradient boosting survival tree with applications in credit scoring","volume":"73","author":"Bai","year":"2022","journal-title":"J. Oper. Res. Soc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.matpr.2022.03.515","article-title":"Random Forest Regressor based superconductivity materials investigation for critical temperature prediction","volume":"66","author":"Revathy","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110400","DOI":"10.1016\/j.mtcomm.2024.110400","article-title":"XGBoost-based prediction of electrical properties for anode aluminium foil","volume":"41","author":"Zhang","year":"2024","journal-title":"Mater. Today Commun."},{"key":"ref_31","first-page":"3146","article-title":"Lightgbm: A highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41524-022-00883-8","article-title":"Topological feature engineering for machine learning based halide perovskite materials design","volume":"8","author":"Anand","year":"2022","journal-title":"npj Comput. Mater."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"435","DOI":"10.3390\/make6010022","article-title":"High-Throughput Ensemble-Learning- Driven Band Gap Prediction of Double Perovskites Solar Cells Absorber","volume":"6","author":"Djeradi","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1016\/j.egyr.2024.03.020","article-title":"Investigating boosting techniques\u2019 efficacy in feature selection: A comparative analysis","volume":"11","author":"Ahmed","year":"2024","journal-title":"Energy Rep."}],"container-title":["Molecules"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1420-3049\/30\/11\/2378\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:43:24Z","timestamp":1760031804000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1420-3049\/30\/11\/2378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,29]]},"references-count":34,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["molecules30112378"],"URL":"https:\/\/doi.org\/10.3390\/molecules30112378","relation":{},"ISSN":["1420-3049"],"issn-type":[{"value":"1420-3049","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,29]]}}}