Fast Predictions of Liquid-Phase Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks

09 December 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the atomistic configurations of reactant-solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable fast predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a computational implementation, which we call SolventNet, and train it using experimental reaction data for seven biomass-derived oxygenates in water-cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid screening of solvent systems and identification of improved biomass conversion conditions.

Keywords

molecular dynamics
machine learning
biomass conversion
simulation
convolutional neural network
solvent effects
acid-catalyzed
catalysis
computational chemistry
solvent screening

Supplementary materials

Title
Description
Actions
Title
chew van lehn fast predictions SI
Description
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.