Machine Learning Meets Quantum Chemistry Using Theory Data and Experiments to Design Catalysts
Catalysis is the cornerstone of modern chemical industry. Catalysts not only convert the natural resources into products we can use in our daily lives, but also aid to solve the hazardous side reaction issues. A suitable catalyst should minimize the energy penalty of the reaction and maximize the selectivity to desired products. This webinar will discuss some of the ways MedeA can be used to understand reaction mechanisms, then incorporate machine learning technics to accelerate catalysts discovery and advance catalysis theory.
Explore reaction pathways with MedeA tools
Calculate surface strain effect of core-shell nanoparticles
Quantify alloy effect on catalysts
Study biopolymer interaction effect on catalysts
Operating potential effect on the stability of electro-catalysts
High-throughput screening of bimetallic materials
Electronic structure of adsorbates on catalysts
Bayesian learning approach to predict site reactivity
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Training: Orbital Level Understanding of Adsorbate-Surface Interactions in Catalysis