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Multi-fidelity Bayesian Optimization of Nanoporous Materials for Xe/Kr Separations

Offered By: ATOMS UFRJ via YouTube

Tags

Bayesian Optimization Courses Machine Learning Courses Materials Science Courses Computational Chemistry Courses

Course Description

Overview

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Explore multi-fidelity Bayesian optimization techniques for enhancing Xe/Kr separations in nanoporous materials through this insightful 40-minute virtual seminar presented by Prof. Cory Simon from Oregon State University. Hosted by the Atoms® group on July 25th, 2024, delve into advanced strategies for optimizing material properties and separation processes. Gain valuable knowledge on the application of Bayesian methods in materials science, specifically focusing on xenon and krypton separation technologies. Learn how this cutting-edge approach can revolutionize the design and selection of nanoporous materials for more efficient gas separation processes.

Syllabus

Cory Simon - Multi-fidelity Bayesian optimization of nanoporous materials for Xe/Kr separations


Taught by

ATOMS UFRJ

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