The application of Artificial Intelligence (AI) and Machine Learning (ML) to catalysis is revolutionizing the way researchers approach catalyst discovery, optimization, and reaction mechanism understanding. By using AI algorithms, researchers can now analyze and predict the complex relationships between catalyst composition, structure, and reaction outcomes, which were previously difficult to map manually. One of the primary advantages of Artificial Intelligence and Machine Learning in catalysis is their ability to process large datasets generated from high-throughput screening and experimental results. These algorithms can identify subtle correlations between catalyst properties and reaction performance, leading to the discovery of more efficient and selective catalysts. For example, ML models can be trained to recognize the structural features of catalysts that contribute to their reactivity, allowing for the design of new catalysts with optimized properties for specific reactions. In addition to designing catalysts, AI and ML are also being applied to reaction mechanism analysis, where they can model the sequence of steps involved in catalytic processes, identifying intermediates, transition states, and energy profiles.
Stanislaw Dzwigaj
Sorbonne University, France
Dai Yeun Jeong
Asia Climate Change Education Center, Korea, Republic of
Enrico Paris
CREA-IT & DIAEE, Italy
Hanna Kierzkowska Pawlak
Lodz University of Technology, Poland
Ling Yin
Cornell University, United States
Tsitsishvili Vladimer
Petre Melikishvili Institute of Physical and Organic Chemistry at the Tbilisi State University, Georgia
Rafia Ahmad
King Abdullah University of Science and Technology, Saudi Arabia
Eun Han Lee
Korea Institute of Energy Research, Korea, Republic of
Neul Ha
Sungkyunkwan University, Korea, Republic of
Shailza Sharma
RMIT University, AustraliaSubmit your abstract Today
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