Approximate sampling using an accelerated metropolis-hastings based on bayesian optimization and gaussian processes
AJ Chowdhury, G Terejanu - arXiv preprint arXiv:1910.09347, 2019 - arxiv.org
Markov Chain Monte Carlo (MCMC) methods have a drawback when working with a target
distribution or likelihood function that is computationally expensive to evaluate, specially …
distribution or likelihood function that is computationally expensive to evaluate, specially …
A Machine Learning Based Approach to Accelerate Catalyst Discovery
AJ Chowdhury - 2020 - search.proquest.com
Computational catalysis, in contrast to experimental catalysis, uses approximations such as
density functional theory (DFT) to compute properties of reaction intermediates. But DFT …
density functional theory (DFT) to compute properties of reaction intermediates. But DFT …