On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Je, Lisa; Huber, George W.; Van Lehn, Reid C.; Zavala, Victor M.

doi:10.1016/j.coche.2022.100796

Title: On the integration of molecular dynamics, data science, and experiments for studying solvent effects on catalysis

Journal Article · 02 February 2022 · Current Opinion in Chemical Engineering

DOI: https://doi.org/10.1016/j.coche.2022.100796 · OSTI ID:1863591

Je, Lisa ^[1]; Huber, George W. ^[2]; Van Lehn, Reid C. ^[2]; Zavala, Victor M. ^[2]

Univ. of Wisconsin, Madison, WI (United States); University of Wisconsin-Madison Department of Chemical and Biological Engineering
Univ. of Wisconsin, Madison, WI (United States)

Computational workflows that combine molecular dynamics (MD) simulations and emerging data-centric (DC) methods can accelerate the screening and analysis of solvent systems experimentally and computationally. Here, MD simulations provide atomic positions and velocities of reactant, solvent, and catalyst materials that can be manipulated into data representations that in turn can be used by DC techniques to conduct predictive modeling, feature extraction, and experimental design. For liquid-phase catalytic applications, emerging DC techniques such as Convolutional and Graph Neural Networks (CNN/GNN), Topological Data Analysis (TDA), and Active Learning (AL) can leverage MD and experimental data to quickly predict solvent effects on reaction outcomes. For instance, in recent studies, 3D solvent environments obtained with MD have been exploited by CNNs to predict experimental reaction rates for homogeneous acid-catalyzed lignocellulosic processes. In this perspective, we discuss basic principles of DC methods and how these can be combined with MD to enable high-throughput screening of solvent selection for diverse catalysis applications.

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Research Organization:: Univ. of Wisconsin, Madison, WI (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0018409

OSTI ID:: 1863591

Journal Information:: Current Opinion in Chemical Engineering, Journal Name: Current Opinion in Chemical Engineering Vol. 36; ISSN 2211-3398

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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