Mikhail Askerka
Papers
1
Total Citations
145
H-Index
1
About
Mikhail Askerka is a leading figure in computational materials science, with a primary focus on accelerating the discovery and optimization of perovskite solar cells through machine learning. His most impactful work, "Machine-Learning-Accelerated Perovskite Crystallization" (2020, 145 citations), pioneered the use of data-driven models to predict and control crystallization pathways, dramatically reducing the trial-and-error in developing high-efficiency devices. By integrating quantum chemistry, high-throughput screening, and artificial intelligence, Askerka has established a framework that enables researchers to rationally design perovskite formulations with superior stability and performance. His contributions have not only advanced fundamental understanding of crystallization kinetics but also provided practical tools for the renewable energy community. With over 145 citations on this seminal paper alone, Askerka’s work is widely recognized for bridging the gap between computational prediction and experimental validation, marking him as a key innovator in the field of AI-driven materials engineering.
Research Focus
Key Achievements
Top Papers
- 1Machine-Learning-Accelerated Perovskite Crystallization145 citations · 2020