Machine-LearnedForce Field for Molecular DynamicsSimulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

Ammonia (NH<sub>3</sub>) is one of the most important industrial chemicals. The conventional NH<sub>3</sub> synthesis methodthe Haber–Bosch processconverts atmospheric nitrogen (N<sub>2</sub>) into NH<sub>3</sub> using H<sub>2</sub> with an iron (Fe) catalyst. However, this process requires high pressures (100–200 atm) and temperatures (700–800 K) near thermal equilibrium. Recently, Fe-based nanocatalysts have been reported to produce promising NH<sub>3</sub> yields under atmospheric pressures and temperature-modulated nonequilibrium conditions. Understanding the mechanism of nonequilibrium catalysis with programmed temperature variation could help to optimize this fully electrified and less energy-intensive process. Although reactive molecular dynamics (RMD) simulations can be a useful tool to model nonequilibrium catalytic processes, they require the development of accurate force fields (i.e., interatomic potentials). Here, we present a machine-learned (ML) force field within the Deep Potential MD (DPMD) framework, trained using periodic density functional theory (DFT) calculations, to model NH<sub>3</sub> synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N<sub>2</sub>, *H<sub>2</sub>, *NH, *NH<sub>2</sub>, and *NH<sub>3</sub>. We generated the DFT data from static models of elementary reactions on the most stable (110) surface of body-centered cubic Fe, which then were augmented by data from constant number of particles–volume–temperature (NVT) DFT-MD trajectories at various temperatures. Finally, we utilized the fully optimized ML force field to investigate reaction dynamics at an Fe(110) surface at linearly increasing temperatures using NVT-DPMD simulations. Our simulations indicate that pulsed temperature ramping could prove favorable for NH<sub>3</sub> synthesis. For example, we conducted ramping under multiple sets of conditions: (i) from 900 to 1200 K over periods of 0.1–0.3 ns for Fe surfaces precovered with N or NH along with H; and (ii) from 300 to 600 K over 0.1–0.3 ns for Fe surfaces precovered with NH<sub>3</sub>. While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH<sub>3</sub> synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

Publication Details

Journal
Figshare
Published
2025-02-27
DOI
https://doi.org/10.1021/acs.jpcc.4c07596.s001
Primary Topic
Genetic diversity and population structure
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article

Machine-LearnedForce Field for Molecular DynamicsSimulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

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Genetic diversity and population structure
article

Machine-LearnedForce Field for Molecular DynamicsSimulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

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Abstract

Ammonia (NH<sub>3</sub>) is one of the most important industrial chemicals. The conventional NH<sub>3</sub> synthesis methodthe Haber–Bosch processconverts atmospheric nitrogen (N<sub>2</sub>) into NH<sub>3</sub> using H<sub>2</sub> with an iron (Fe) catalyst. However, this process requires high pressures (100–200 atm) and temperatures (700–800 K) near thermal equilibrium. Recently, Fe-based nanocatalysts have been reported to produce promising NH<sub>3</sub> yields under atmospheric pressures and temperature-modulated nonequilibrium conditions. Understanding the mechanism of nonequilibrium catalysis with programmed temperature variation could help to optimize this fully electrified and less energy-intensive process. Although reactive molecular dynamics (RMD) simulations can be a useful tool to model nonequilibrium catalytic processes, they require the development of accurate force fields (i.e., interatomic potentials). Here, we present a machine-learned (ML) force field within the Deep Potential MD (DPMD) framework, trained using periodic density functional theory (DFT) calculations, to model NH<sub>3</sub> synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N<sub>2</sub>, *H<sub>2</sub>, *NH, *NH<sub>2</sub>, and *NH<sub>3</sub>. We generated the DFT data from static models of elementary reactions on the most stable (110) surface of body-centered cubic Fe, which then were augmented by data from constant number of particles–volume–temperature (NVT) DFT-MD trajectories at various temperatures. Finally, we utilized the fully optimized ML force field to investigate reaction dynamics at an Fe(110) surface at linearly increasing temperatures using NVT-DPMD simulations. Our simulations indicate that pulsed temperature ramping could prove favorable for NH<sub>3</sub> synthesis. For example, we conducted ramping under multiple sets of conditions: (i) from 900 to 1200 K over periods of 0.1–0.3 ns for Fe surfaces precovered with N or NH along with H; and (ii) from 300 to 600 K over 0.1–0.3 ns for Fe surfaces precovered with NH<sub>3</sub>. While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH<sub>3</sub> synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

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Genetic diversity and population structure
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