Chemistry and Materials Science Seminar: Machine Learning in Energy Materials Discovery and AI in Chemistry Education
Chemistry and Materials Science Seminar
Machine Learning in Energy Materials Discovery and AI in Chemistry Education
Rakesh Suthar, PhD
Postdoctoral Research Fellow
School of Chemistry and Materials Science
Rochester Institute of Technology
Event Details: The rapid evolution of artificial intelligence (AI) and machine learning (ML) is transforming the landscape of chemistry, from materials discovery to science education. In energy materials research, ML algorithms enable accelerated material screening, property prediction, and process optimization for desired electrochemical, catalytic, and photovoltaic properties. In this seminar, I will discuss our latest work, which utilizes ML–guided Bayesian optimization to screen stable wide-band-gap perovskite films for space applications. The presentation will focus on how data-driven models that integrate physics-informed descriptors with experimental chemistry can substantially reduce the time and cost required to discover next-generation photovoltaic materials for sustainable energy applications. As AI-driven methods become central to chemical research, there is a pressing need to prepare chemists with the computational and data-science skills to use these tools effectively. To meet this need, we introduced a new course, AI for Chemistry, where students explore molecular descriptors, feature engineering, predictive modeling, inverse design, and large language models, thereby bridging chemical intuition with modern machine learning. This seminar will also highlight how this course cultivates AI fluency and chemical insight, equipping students to thrive in a rapidly evolving chemical industry.
Intended Audience: All are Welcome!
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