Dr. Ashish Kulkarni is an Assistant Professor in the Department of Chemistry at the Indian Institute of Technology (IIT) Tirupati. His research focuses on halide perovskite materials (both lead-based and lead-free) and organic semiconductors for next-generation high-efficiency solar cells. 

He earned his PhD under the supervision of Prof. Tsutomu Miyasaka, a pioneer in perovskite solar cells, at Toin University of Yokohama, Japan. During his time in Prof. Miyasaka’s group, Dr. Kulkarni was a JSPS doctoral and postdoctoral fellow, working on lead-based and lead-free perovskite-like materials for photovoltaic applications. His contributions earned him the prestigious "Emerging Young Scientist" award from the Materials Research Society of Japan (MRS-J). 

Following this, he joined the research groups of Prof. Thomas Kirchartz and Prof. Michael Saliba at Forschungszentrum Jülich GmbH, Germany, where he worked from March 2020 to February 2023. His research there focused on wide-bandgap perovskite solar cells for silicon-perovskite tandem applications, with an emphasis on interfacial interactions, charge transport mechanisms between perovskites and organic semiconductor transport layers, and device degradation pathways. 

Before joining IIT Tirupati, Dr. Kulkarni worked in Prof. Sanjay Mathur’s group at the University of Cologne as a DAAD-PRIME fellow. His research during this time aimed at enhancing the thermal stability of wide-bandgap all-inorganic perovskite solar cells and developing eco-friendly fabrication methods using green solvents. 

Research Interests:
Dr. Kulkarni's research focuses on developing innovative methods to control the nucleation and crystallization dynamics of emerging semiconductor materials (halide perovskite and organic materials) for high-efficiency next generation solar cells (including semi-transparent and flexible solar cells) for various energy applications. 

Dr. Ashish Kulkarni is also interested in leveraging machine learning (ML) and artificial intelligence (AI) for predictive analysis and optimization in energy generation. His research explores the application of ML algorithms to model, analyze, and enhance the performance of solar cells, particularly perovskite-based and tandem architectures. By utilizing data-driven approaches (also see Nature Energy, 2022, 7, 107–115), he aims to improve material selection, device stability, and efficiency predictions, thereby accelerating the development of next-generation photovoltaic technologies.