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revised on 30.04.2020 and posted on 04.05.2020by Alex Chew, Shengli Jiang, Weiqi Zhang, Victor Zavala, Reid Van Lehn
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.