ChE-605: AI in chemistry and beyond:Highlights in the field

Lectures in this course (9)

Delves into the automation of chemical synthesis through catalyst discovery and optimization using machine learning and computational chemistry.

Covers machine learning approaches for material design, practical examples, and software tools for research.

Explores machine learning in chemistry, focusing on Bayesian reaction optimization and shifting the experimental burden from humans to machines.

Explores the application of deep generative models in drug discovery, focusing on designing small molecules and optimizing molecular structures.

Explores AI4Science at Microsoft, focusing on catalyst design, reaction mechanisms, and material discovery using Clifford algebra.

Explores the use of generative models for discovering novel molecules in molecular design.

Explores multi-task learning for accelerated chemical reaction optimization, showcasing challenges, automated workflows, and optimization algorithms.

Explores leveraging machine learning for organic synthesis, focusing on predicting reaction yields and dataset predictability.

Explores machine-learned force fields for accurate molecular simulations and the solution of the electronic Schrödinger equation.

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