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.
Dai Yeun Jeong
Asia Climate Change Education Center, Korea, Republic of
Sergey Suchkov
N.D. Zelinskii Institute for Organic Chemistry of the Russian Academy of Sciences, Russian Federation
Enrico Paris
CREA-IT & DIAEE, Italy
Tokeer Ahmad
Jamia Millia Islamia, India
Rabeharitsara Andry Tahina
GPCI-ESPA Antananarivo University, Madagascar
Jiri Dedecek
J Heyrovsky Institute of Physical Chemistry , Czech Republic
Uday Som
Research and Development Engineer, Japan
Vladimir G Chigrinov
Hong Kong University of Science and Technology, Russian Federation
Osman Adiguzel
Firat University, TurkeySubmit your abstract Today
Title : Distant binuclear vanadium V(II) cationic sites in zeolites and their reactivity
Jiri Dedecek, J Heyrovsky Institute of Physical Chemistry , Czech Republic
Title : Advanced nanostructures for carbon neutrality and sustainable H₂ energy
Tokeer Ahmad, Jamia Millia Islamia, India
Title : Personalized and Precision Medicine (PPM) as a unique healthcare model via bi-odesign, bio- and chemical engineering, translational applications, and upgraded business modeling to secure the human healthcare and biosafety
Sergey Suchkov, N.D. Zelinskii Institute for Organic Chemistry of the Russian Academy of Sciences, Russian Federation
Title : Antibody-proteases as a generation of unique biomarkers, biocatalysts, potential targets and translational tools towards nanodesign-driven biochemical engineering and precision medical practice
Sergey Suchkov, N.D. Zelinskii Institute for Organic Chemistry of the Russian Academy of Sciences, Russian Federation
Title : Dimethyl ether synthesis from syngas over Cu-Zn/Al2O3 catalysts prepared using the Sol-Gel method
Uday Som, Research and Development Engineer, Japan
Title : Influence of various catalysts on H₂ enhancement and CO2 capture during syngas upgrading
Enrico Paris, CREA-IT & DIAEE, Italy
Title : Photoaligned azodye nanolayers : New nanotechnology for liquid crystal devices
Vladimir G Chigrinov, Hong Kong University of Science and Technology, Russian Federation
Title : Oxidation of methane to methanol over pairs of transition metal ions stabilized in the zeolite matrices
Jiri Dedecek, J Heyrovsky Institute of Physical Chemistry , Czech Republic
Title : The Concept and Implications of Low Carbon Green Growth
Dai Yeun Jeong, Asia Climate Change Education Center, Korea, Republic of
Title : Memory characteristics and diffusionless phase transformations in shape memory alloys
Osman Adiguzel, Firat University, Turkey