机器学习力场在铁催化剂上非平衡氨合成分子动力学模拟中的应用

IF 3.2 3区 化学 Q2 CHEMISTRY, PHYSICAL
Aditya Dilip Lele*, Zhiyu Shi, Shrey Khetan, Emily A. Carter, John Mark P. Martirez and Yiguang Ju, 
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引用次数: 0

摘要

氨(NH3)是最重要的工业化学品之一。传统的NH3合成方法──Haber-Bosch法──利用H2和铁(Fe)催化剂将大气中的氮气(N2)转化为NH3。然而,这一过程需要接近热平衡的高压(100-200 atm)和温度(700-800 K)。最近,铁基纳米催化剂在常压和温度调节的非平衡条件下产生了有希望的NH3产率。了解具有程序化温度变化的非平衡催化机制有助于优化这一完全电气化和低能耗的过程。尽管反应分子动力学(RMD)模拟可以是模拟非平衡催化过程的有用工具,但它们需要开发精确的力场(即原子间电位)。在这里,我们在Deep Potential MD (DPMD)框架内提出了一个机器学习(ML)力场,使用周期密度泛函理论(DFT)计算进行训练,以模拟具有不同表面吸附(如*N, *H, *N2, *H2, *NH, *NH2和*NH3)的Fe催化剂上的NH3合成。我们从最稳定(110)的体心立方铁表面上的元素反应的静态模型中生成了DFT数据,然后通过在不同温度下恒定数量的粒子-体积-温度(NVT) DFT- md轨迹的数据进行了扩充。最后,我们利用完全优化的ML力场,利用NVT-DPMD模拟研究了线性升高温度下Fe(110)表面的反应动力学。我们的模拟结果表明,脉冲升温有利于NH3的合成。例如,我们在多种条件下进行了爬坡:(i)在0.1-0.3 ns的时间内,从900到1200 K对N或NH和H预先覆盖的Fe表面进行爬坡;(ii)在300 ~ 600 K范围内,在0.1 ~ 0.3 ns范围内,Fe表面被NH3预先覆盖。虽然到目前为止,我们的模拟仅限于短时间尺度(非常快速的加热),但这些观察结果揭示了在使用脉冲加热和冷却的新型温度调制非平衡催化反应器中实现高NH3合成速率的机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine-Learned Force Field for Molecular Dynamics Simulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

Machine-Learned Force Field for Molecular Dynamics Simulations of Nonequilibrium Ammonia Synthesis on Iron Catalysts

Ammonia (NH3) is one of the most important industrial chemicals. The conventional NH3 synthesis method─the Haber–Bosch process─converts atmospheric nitrogen (N2) into NH3 using H2 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 NH3 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 NH3 synthesis on Fe catalysts with various surface adsorbates such as *N, *H, *N2, *H2, *NH, *NH2, and *NH3. 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 NH3 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 NH3. While our simulations so far are limited to short time scales (very rapid heating), these observations shed light on the mechanism of the high NH3 synthesis rate achieved in a novel temperature-modulated nonequilibrium catalytic reactor using pulsed heating and cooling.

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来源期刊
The Journal of Physical Chemistry C
The Journal of Physical Chemistry C 化学-材料科学:综合
CiteScore
6.50
自引率
8.10%
发文量
2047
审稿时长
1.8 months
期刊介绍: The Journal of Physical Chemistry A/B/C is devoted to reporting new and original experimental and theoretical basic research of interest to physical chemists, biophysical chemists, and chemical physicists.
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