{"title":"利用DFT、SCAPS-1D和机器学习技术综合分析太阳能电池用Sr3PCl3吸收剂","authors":"Md. Hafizur Rahman , Foysal Ahmed , Noureddine Elboughdiri , Karim KRIAA , Md. Sharif Uddin , Md. Azizur Rahman , Mst. Nazifa Tasnim , Imed Boukhris , Ali Akremi , Jothi Ramalingam Rajabathar , Mohd Taukeer Khan","doi":"10.1016/j.poly.2025.117676","DOIUrl":null,"url":null,"abstract":"<div><div>This study presents an integrated computational approach combining Density Functional Theory (DFT), SCAPS-1D simulations, and machine learning to design and optimize lead-free Sr<sub>3</sub>PCl<sub>3</sub> perovskite-based solar cells. Although perovskite solar cells exhibit outstanding optoelectronic properties. However, the environmental and health hazards associated with lead-based materials present a major limitation. To address this, Sr<sub>3</sub>PCl<sub>3</sub> is investigated as a potential absorber material. DFT calculations reveal that Sr<sub>3</sub>PCl<sub>3</sub> possesses a direct bandgap of 1.641 eV, high absorption coefficients, and excellent stability, making it a promising candidate for photovoltaic applications. Device performance was analyzed using SCAPS-1D, examining various electron transport layers (ETLs), including WS<sub>2</sub>, CdS, SnS<sub>2</sub>, and ZnS. Optimization of absorber thickness and defect density was performed to enhance efficiency. Among the tested configurations, the structure employing SnS<sub>2</sub> as the ETL achieved the highest power conversion efficiency (PCE) of 18.58 %. Other configurations using WS<sub>2</sub>, CdS, and ZnS exhibited PCEs of 17.70 %, 18.35 %, and 14.15 %, respectively. To further accelerate device optimization, machine learning models—specifically Ridge regression and CatBoost—were trained on 2187 SCAPS-1D simulation results. These models accurately predicted solar cell performance based on key parameters such as absorber thickness, defect density, and ETL characteristics. To enhance interpretability, techniques such as heatmaps and SHAP (SHapley Additive exPlanations) analysis were utilized to examine the influence of key parameters on device efficiency. This comprehensive framework, integrating first-principles calculations, numerical simulations, and machine learning, provides valuable insights into the development of stable, high-efficiency, lead-free perovskite solar cells. The findings underscore the potential of Sr<sub>3</sub>PCl<sub>3</sub> as an environmentally friendly absorber material, advancing its prospects for next-generation optoelectronic applications.</div></div>","PeriodicalId":20278,"journal":{"name":"Polyhedron","volume":"280 ","pages":"Article 117676"},"PeriodicalIF":2.6000,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comprehensive analysis of Sr3PCl3 absorber for solar cells using DFT, SCAPS-1D, and machine learning techniques\",\"authors\":\"Md. Hafizur Rahman , Foysal Ahmed , Noureddine Elboughdiri , Karim KRIAA , Md. Sharif Uddin , Md. Azizur Rahman , Mst. Nazifa Tasnim , Imed Boukhris , Ali Akremi , Jothi Ramalingam Rajabathar , Mohd Taukeer Khan\",\"doi\":\"10.1016/j.poly.2025.117676\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study presents an integrated computational approach combining Density Functional Theory (DFT), SCAPS-1D simulations, and machine learning to design and optimize lead-free Sr<sub>3</sub>PCl<sub>3</sub> perovskite-based solar cells. Although perovskite solar cells exhibit outstanding optoelectronic properties. However, the environmental and health hazards associated with lead-based materials present a major limitation. To address this, Sr<sub>3</sub>PCl<sub>3</sub> is investigated as a potential absorber material. DFT calculations reveal that Sr<sub>3</sub>PCl<sub>3</sub> possesses a direct bandgap of 1.641 eV, high absorption coefficients, and excellent stability, making it a promising candidate for photovoltaic applications. Device performance was analyzed using SCAPS-1D, examining various electron transport layers (ETLs), including WS<sub>2</sub>, CdS, SnS<sub>2</sub>, and ZnS. Optimization of absorber thickness and defect density was performed to enhance efficiency. Among the tested configurations, the structure employing SnS<sub>2</sub> as the ETL achieved the highest power conversion efficiency (PCE) of 18.58 %. Other configurations using WS<sub>2</sub>, CdS, and ZnS exhibited PCEs of 17.70 %, 18.35 %, and 14.15 %, respectively. To further accelerate device optimization, machine learning models—specifically Ridge regression and CatBoost—were trained on 2187 SCAPS-1D simulation results. These models accurately predicted solar cell performance based on key parameters such as absorber thickness, defect density, and ETL characteristics. To enhance interpretability, techniques such as heatmaps and SHAP (SHapley Additive exPlanations) analysis were utilized to examine the influence of key parameters on device efficiency. This comprehensive framework, integrating first-principles calculations, numerical simulations, and machine learning, provides valuable insights into the development of stable, high-efficiency, lead-free perovskite solar cells. The findings underscore the potential of Sr<sub>3</sub>PCl<sub>3</sub> as an environmentally friendly absorber material, advancing its prospects for next-generation optoelectronic applications.</div></div>\",\"PeriodicalId\":20278,\"journal\":{\"name\":\"Polyhedron\",\"volume\":\"280 \",\"pages\":\"Article 117676\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Polyhedron\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0277538725002906\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, INORGANIC & NUCLEAR\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Polyhedron","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0277538725002906","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, INORGANIC & NUCLEAR","Score":null,"Total":0}
Comprehensive analysis of Sr3PCl3 absorber for solar cells using DFT, SCAPS-1D, and machine learning techniques
This study presents an integrated computational approach combining Density Functional Theory (DFT), SCAPS-1D simulations, and machine learning to design and optimize lead-free Sr3PCl3 perovskite-based solar cells. Although perovskite solar cells exhibit outstanding optoelectronic properties. However, the environmental and health hazards associated with lead-based materials present a major limitation. To address this, Sr3PCl3 is investigated as a potential absorber material. DFT calculations reveal that Sr3PCl3 possesses a direct bandgap of 1.641 eV, high absorption coefficients, and excellent stability, making it a promising candidate for photovoltaic applications. Device performance was analyzed using SCAPS-1D, examining various electron transport layers (ETLs), including WS2, CdS, SnS2, and ZnS. Optimization of absorber thickness and defect density was performed to enhance efficiency. Among the tested configurations, the structure employing SnS2 as the ETL achieved the highest power conversion efficiency (PCE) of 18.58 %. Other configurations using WS2, CdS, and ZnS exhibited PCEs of 17.70 %, 18.35 %, and 14.15 %, respectively. To further accelerate device optimization, machine learning models—specifically Ridge regression and CatBoost—were trained on 2187 SCAPS-1D simulation results. These models accurately predicted solar cell performance based on key parameters such as absorber thickness, defect density, and ETL characteristics. To enhance interpretability, techniques such as heatmaps and SHAP (SHapley Additive exPlanations) analysis were utilized to examine the influence of key parameters on device efficiency. This comprehensive framework, integrating first-principles calculations, numerical simulations, and machine learning, provides valuable insights into the development of stable, high-efficiency, lead-free perovskite solar cells. The findings underscore the potential of Sr3PCl3 as an environmentally friendly absorber material, advancing its prospects for next-generation optoelectronic applications.
期刊介绍:
Polyhedron publishes original, fundamental, experimental and theoretical work of the highest quality in all the major areas of inorganic chemistry. This includes synthetic chemistry, coordination chemistry, organometallic chemistry, bioinorganic chemistry, and solid-state and materials chemistry.
Papers should be significant pieces of work, and all new compounds must be appropriately characterized. The inclusion of single-crystal X-ray structural data is strongly encouraged, but papers reporting only the X-ray structure determination of a single compound will usually not be considered. Papers on solid-state or materials chemistry will be expected to have a significant molecular chemistry component (such as the synthesis and characterization of the molecular precursors and/or a systematic study of the use of different precursors or reaction conditions) or demonstrate a cutting-edge application (for example inorganic materials for energy applications). Papers dealing only with stability constants are not considered.