Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2407.15573 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866929430349217792 |
|---|---|
| author | Kumar, Upendra Kim, Hyeon Woo Maurya, Gyanendra Kumar Raj, Bincy Babu Singh, Sobhit Kushwaha, Ajay Kumar Cho, Sung Beom Ko, Hyunseok |
| author_facet | Kumar, Upendra Kim, Hyeon Woo Maurya, Gyanendra Kumar Raj, Bincy Babu Singh, Sobhit Kushwaha, Ajay Kumar Cho, Sung Beom Ko, Hyunseok |
| contents | The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl$_4$, Rb$_3$Mn$_2$Cl$_9$, Rb$_4$MnCl$_6$, Rb$_3$MnCl$_5$, RbMn$_2$Cl$_7$, RbMn$_4$Cl$_9$, and CsIn$_2$Cl$_7$. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl$_4$ is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15573 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning-Enhanced Design of Lead-Free Halide Perovskite Materials Using Density Functional Theory Kumar, Upendra Kim, Hyeon Woo Maurya, Gyanendra Kumar Raj, Bincy Babu Singh, Sobhit Kushwaha, Ajay Kumar Cho, Sung Beom Ko, Hyunseok Materials Science The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl$_4$, Rb$_3$Mn$_2$Cl$_9$, Rb$_4$MnCl$_6$, Rb$_3$MnCl$_5$, RbMn$_2$Cl$_7$, RbMn$_4$Cl$_9$, and CsIn$_2$Cl$_7$. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl$_4$ is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials. |
| title | Machine Learning-Enhanced Design of Lead-Free Halide Perovskite Materials Using Density Functional Theory |
| topic | Materials Science |
| url | https://arxiv.org/abs/2407.15573 |