| Titre : |
Theoretical study of emerging perovskite materials for solar energy conversion |
| Type de document : |
document multimédia |
| Auteurs : |
Sabrina Djeradi, Auteur ; Taher Daham, Directeur de thèse ; Mohamed Abdelilah Fadla, Directeur de thèse |
| Editeur : |
Laghouat : Université Amar Telidji - Département des sciences de la matière |
| Année de publication : |
2025 |
| Importance : |
102 p. |
| Accompagnement : |
1 disque optique numérique (CD-ROM) |
| Note générale : |
Option : Materials physics |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Double perovskites Mixed-halide alloys CsPb(I1−xBrx)3 Machine learn- ing Ensemble learning Density functional theory (DFT) Special quasirandom structure (SQS) Optoelectronic properties Tandem solar cells |
| Résumé : |
The efficiency of perovskite solar cells (PSCs) has increased quickly, reaching 26.7% for single-junction devices and over 34% when combined with silicon . PSCs have excel- lent prospects for next-generation photovoltaics due to their great optical absorption, high carrier mobility, and adjustable direct band gaps. Commercialization is still severely ham- pered by insufficient stability under environmental stress, where long-term stability is cru- cial. The stability problem is addressed in this thesis by combining two approaches into a single materials design process. First, the band gap of double perovskites is predicted us- ing composition-based characteristics alone employing a high-through ensemble machine learning algorithms. As a result, stable and effective candidates may be quickly screened without incurring the computational expense of comprehensive quantum computations. Second, spin-orbit coupling (SOC) and hybrid HSE06 functionals are used in the density functional theory calculations (DFT) for a specific type of mixed-halideCsPb(I1–xBrx)3 alloys. In order to capture atomic disorder and allow for a thorough examination of struc- tural, electronic, and optical characteristics, these systems are represented using the spe- cial quasirandom structure (SQS) technique.The workflow connects the two approaches in which DFT refines and tests the stability and optoelectronic behavior of potential com- positions identified by machine learning at the atomic level. The results indicate that, with only a little decrease in apparent absorption, bromide inclusion improves structural stabil- ity, linearly adjusts the band gap, and modifies band offsets based on surface termination.
This study shows a productive, data-driven approach to finding and improving stable, high-performance perovskites by combining predictive modeling with focused first-principles simulations. The strategy may be used to balance environmental resilience and efficiency when customizing materials for silicon-based tandem solar cells. |
| note de thèses : |
Thèse de doctorat en physique |
Theoretical study of emerging perovskite materials for solar energy conversion [document multimédia] / Sabrina Djeradi, Auteur ; Taher Daham, Directeur de thèse ; Mohamed Abdelilah Fadla, Directeur de thèse . - Laghouat : Université Amar Telidji - Département des sciences de la matière, 2025 . - 102 p. + 1 disque optique numérique (CD-ROM). Option : Materials physics Langues : Anglais ( eng)
| Mots-clés : |
Double perovskites Mixed-halide alloys CsPb(I1−xBrx)3 Machine learn- ing Ensemble learning Density functional theory (DFT) Special quasirandom structure (SQS) Optoelectronic properties Tandem solar cells |
| Résumé : |
The efficiency of perovskite solar cells (PSCs) has increased quickly, reaching 26.7% for single-junction devices and over 34% when combined with silicon . PSCs have excel- lent prospects for next-generation photovoltaics due to their great optical absorption, high carrier mobility, and adjustable direct band gaps. Commercialization is still severely ham- pered by insufficient stability under environmental stress, where long-term stability is cru- cial. The stability problem is addressed in this thesis by combining two approaches into a single materials design process. First, the band gap of double perovskites is predicted us- ing composition-based characteristics alone employing a high-through ensemble machine learning algorithms. As a result, stable and effective candidates may be quickly screened without incurring the computational expense of comprehensive quantum computations. Second, spin-orbit coupling (SOC) and hybrid HSE06 functionals are used in the density functional theory calculations (DFT) for a specific type of mixed-halideCsPb(I1–xBrx)3 alloys. In order to capture atomic disorder and allow for a thorough examination of struc- tural, electronic, and optical characteristics, these systems are represented using the spe- cial quasirandom structure (SQS) technique.The workflow connects the two approaches in which DFT refines and tests the stability and optoelectronic behavior of potential com- positions identified by machine learning at the atomic level. The results indicate that, with only a little decrease in apparent absorption, bromide inclusion improves structural stabil- ity, linearly adjusts the band gap, and modifies band offsets based on surface termination.
This study shows a productive, data-driven approach to finding and improving stable, high-performance perovskites by combining predictive modeling with focused first-principles simulations. The strategy may be used to balance environmental resilience and efficiency when customizing materials for silicon-based tandem solar cells. |
| note de thèses : |
Thèse de doctorat en physique |
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