Accelerated composition optimization of hybrid perovskites via data-driven materials design, DFT calculations and synthesis

Materials & Design, vol. 260, pp. 114902

Abstract

Hybrid perovskites show promise as low-cost photovoltaics. However, achieving high efficiency necessitates careful optimization of their composition. Traditionally, exploring the vast compositional space is inefficient and time-consuming. This study demonstrates machine learning (ML) techniques to effectively and rapidly map the relationships between perovskite composition and critical band gap energy ( E g ) , leading to the path of finding highly efficient perovskites for solar cell applications. Among these, Random Forest and Gradient Boosting Regression algorithms demonstrated exceptional predictive performance. Compositions with E g values between 1.2 and 2.0 eV were screened for further study. To validate data-driven approach, F A P b ( B r 0.375 I 0.625 ) 3 was selected from the screened data and further studied through Density Functional Theory (DFT) and experimental methods. To the best of our knowledge, no previous studies have analyzed organic-inorganic halide perovskites in such depth, focusing on their crystal structure, stability (offering a new perspective on organic components), and E g . These aspects were thoroughly investigated and validated using ML, DFT, and experimental synthesis with characterization. • ML prediction: Simple ML algorithms can accurately predict perovskite features, even when trained on limited but carefully curated datasets. • Theoretical confirmation: DFT confirmed the predicted crystal structures and E g , providing robust theoretical validation for the ML results. • Experimental validation: The synthesized perovskite properties align closely with ML-predicted and DFT-calculated results, bridging the gap. • Implications for materials design: The alignment between prediction, and synthesis confirms the reliability of ML in designing new materials.

Authors 7

1. Nane Petrosyan

   Yerevan State University · Institute of Chemical Physics NAS RA

   Affiliation as printed

   A.B. Nalbandyan Institue of Chemical Physics, P.Sevak 5/2, Yerevan, 0014, Yerevan, Armenia

   Yerevan State University, Alex Manoogian 1, Yerevan, 0025, Yerevan, Armenia

2. Gurgen Kolotyan

   Institute of Chemical Physics NAS RA

   Affiliation as printed

   A.B. Nalbandyan Institue of Chemical Physics, P.Sevak 5/2, Yerevan, 0014, Yerevan, Armenia

3. Arpine Kozmanyan

   Institute of Chemical Physics NAS RA

   Affiliation as printed

   A.B. Nalbandyan Institue of Chemical Physics, P.Sevak 5/2, Yerevan, 0014, Yerevan, Armenia

4. Hayk Zakaryan

   Yerevan State University

   Affiliation as printed

   Yerevan State University, Alex Manoogian 1, Yerevan, 0025, Yerevan, Armenia

5. Michael Josef Schöning Aachen Institute of Nano- and Biotechnologies (INB)

   FH Aachen

   Affiliation as printed

   Institute of Nano- and Biotechnologies, Aachen University of Applied Sciences, Heinrich-Mußmann-Straße 1, Jülich, 52428, North Rhine-Westphalia, Germany

6. A. V. Asatryan corresponding

   Institute of Chemical Physics NAS RA

   Affiliation as printed

   A.B. Nalbandyan Institue of Chemical Physics, P.Sevak 5/2, Yerevan, 0014, Yerevan, Armenia

7. H.L. Khachatryan

   Institute of Chemical Physics NAS RA

   Affiliation as printed

   A.B. Nalbandyan Institue of Chemical Physics, P.Sevak 5/2, Yerevan, 0014, Yerevan, Armenia