Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations

Abstract

We employ multi-fidelity Bayesian optimization to search a large candidate set of covalent organic frameworks (COFs) for the one with the largest [simulated] equilibrium adsorptive selectivity for xenon (Xe) over krypton (Kr) at room temperature.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2023
Source ID
10.1039/d3dd00117b

Entities

People

  • Aryan Deshwal
  • Cory M Simon
  • Janardhan Rao Doppa
  • Nickolas Gantzler

Organizations

  • Defense Threat Reduction Agency
  • National Science Foundation
  • Oregon State University
  • Washington State University

Tags

Fields of Study

  • Physics

Readers

  • Molecular Photonics/Laser Physics
  • Nanocomposite Materials Science
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms