Journal of Physics Research and Education, Vol. 02

Permanent URI for this collectionhttps://ir.nbu.ac.in/handle/123456789/5611

EDITORIAL NOTE

We are pleased to present the second volume of the departmental journal, Journal of Physics Research and Education, to our readers. The goal of JPRE is to create a unifying impact that encompasses the diverse fields of physics research and education. The current volume has a distinctive aspect of this nature contributed by numerous Ph.D. scholars, postdoctoral scholars, professors, and scientists from various institutions. Apart from the regular research papers, a review and a mini-review are included in the current issue. I must express my deep thanks and gratitude to all the eminent contributors for their scholarly articles to this edition and the entire editorial team for their scholarly support and assistance. Hope this volume will reflect the current state of interdisciplinary research and insightful reviews and will indicate the usefulness of their material to physics education.

Rajat K. Dey
Editor-in-Chief

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Author: search.filters.author.Chatterjee, Suman

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    Design and Development of Mixed Perovskite Solar Cells with High Efficiency and Stability through DFT and AI-Based Design Approaches
    (University of North Bengal, 2025-03) Chatterjee, Suman; Subba, Subham; Talukdar, Avijit; Debnath, Pratik; Sarkar, Joy
    Compared to different solar technologies, Perovskite-based solar cells are preferred by many for their high PCE and cost-effectiveness. Still, building a market-ready solution requires handling various important issues related to stability, how the device is designed, and efficiency. The study presents comprehensive approaches involving Density Functional Theory (DFT), device modeling, and Machine Learning (ML) to improve and evaluate mixed perovskite materials. DFT was utilized to study the electronic structure, energy gap, and defect properties of perovskites. By using SCAPS-1D simulations, different optimization factors were studied. Furthermore, different ML algorithms were trained to find key device properties. The training involved both experiments and simulations to learn from the data and predict how each material would work, allowing for fast screening of various perovskite compositions. Because of this framework, researchers can identify new, efficient materials and learn more about how different compositions affect solar cell performance. This strategy uses DFT modeling, numerical simulation, and data analysis together to improve the speed of developing better perovskite solar cells.