Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 19, Issue 14, Pages 4584-4595Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.2c00816
Keywords
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Funding
- Office of Basic Energy Sciences, U.S. Department of Energy [DE-SC0002128]
- U.S. Department of Energy (DOE) [DE-SC0002128] Funding Source: U.S. Department of Energy (DOE)
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Researchers developed a deep potential machine learning model for simulating chemical reactions in molten alkali carbonate-hydroxide electrolyte containing dissolved CO2. They tested the model against density functional theory calculations and found it accurately simulated the reactions in the melt and extended the observation time.
We developed a deep potential machine learning model for simulations of chemical reactions in molten alkali carbonate-hydroxide electrolyte containing dissolved CO2, using an active learning procedure. We tested the deep neural network (DNN) potential and training procedure against reaction kinetics, chemical composition, and diffusion coefficients obtained from density functional theory (DFT) molecular dynamics calculations. The DNN potential was found to match DFT results for the structural, transport, and short-time chemical reactions in the melt. Using the DNN potential, we extended the time scales of observation to 2 ns in systems containing thousands of atoms, while preserving quantum chemical accuracy. This allowed us to reach chemical equilibrium with respect to several chemical species in the melt. The approach can be generalized for a broad spectrum of chemically reactive systems.
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