Predicting Chemical Reaction Barriers with a Machine Learning Model
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In the past few decades, tremendous advances have been made in the understanding of catalysis at solid surfaces. Despite this, most discoveries of materials for improved catalytic performance are made by a slow trial and error process in an experimental laboratory. Computational simulations have begun to provide a way to rationally design materials for optimizing catalytic performance, but due to the high computational expense of calculating transition state energies, simulations cannot adequately screen the phase space of materials. In this work, we attempt to mitigate this expense by using a machine learning approach to predict the most expensive and most important parameter in a catalyst’s affinity for a reaction: the reaction barrier. Previous methods which used the step reaction energy as the only parameter in a linear regression had a mean absolute error (MAE) on the order of 0.4 eV, too high to be used predictively. In our work, we achieve a MAE of about 0.22 eV, a marked improvement towards the goal of computational prediction of catalytic activity.
KeywordsDensity functional theory Machine learning Kinetic modeling
Support from the U.S. Department of Energy, Office of Basic Energy Science, Chemical Sciences, Geosciences, and Biosciences Division, to the SUNCAT Center for Interface Science and Catalysis is gratefully acknowledged. B.A.R. acknowledges fellowship support from the National Science Foundation Graduate Research Fellowship (Grant No. DGE-114747). This work was supported by a research grant (9455) from VILLUM FONDEN.
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Conflict of interest
The authors have no conflict of interest to declare.
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