TY - JOUR
T1 - Modeling Chemical Reactions in Alkali Carbonate-Hydroxide Electrolytes with Deep Learning Potentials
AU - Mondal, Anirban
AU - Kussainova, Dina
AU - Yue, Shuwen
AU - Panagiotopoulos, Athanassios Z.
N1 - Publisher Copyright:
© 2022 American Chemical Society.
PY - 2023/7/25
Y1 - 2023/7/25
N2 - 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.
AB - 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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U2 - 10.1021/acs.jctc.2c00816
DO - 10.1021/acs.jctc.2c00816
M3 - Article
C2 - 36239670
AN - SCOPUS:85140321600
SN - 1549-9618
VL - 19
SP - 4584
EP - 4595
JO - Journal of Chemical Theory and Computation
JF - Journal of Chemical Theory and Computation
IS - 14
ER -