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使用神经网络潜力的Ti-V-Nb-Mo碳化物MXenes的高通量探索及其作为进化反应催化剂的评估

Mohammed Wasay Mudassir¹, Sriram Goverapet Srinivasan², Mahesh Mynam¹

¹TCS Research, Tata Consultancy Services, Deccan Park, Madhapur, Hyderabad 500081, India.

ACS applied materials & interfaces

|December 28, 2024

概括

为可持续的经济开发高效的催化剂至关重要. 这项研究使用先进的建模探索高的MXenes,识别稳定的组成,并了解它们对进化反应的催化活性.

科学领域:

材料科学材料科学材料科学
催化剂是一种催化剂.
计算化学计算化学

背景情况:

可持续的经济需要高效的,具有成本效益的催化剂来大规模生产.
MXenes (M_n+1X_n) 是具有可调节性质的二维材料,显示为进化反应 (HER) 催化剂的承诺.
高度的MXenes为发现优质材料提供了广的组成空间,但实验性探索具有挑战性.

研究的目的:

开发一个计算模型来探索高的MXene组成和特性.
确定热力学稳定的MXene组合物,并了解过渡金属原子的排列.
为了研究稳定的MXenes在进化反应中的催化性能.

主要方法:

开发了一个神经网络潜力 (NNP) 训练在密度函数理论 (DFT) 数据为 (TiVNbMo_p) _n+1C_n MXenes.
采用混合蒙特卡洛-分子动力学 (MC-MD) 模拟来评估热力学稳定性和原子排列.
相关的电子结构 (氧 p 波段中心) 与催化活性 (吸附能量).

主要成果:

确定热力学稳定性随着 (Mo) 含量增加而增加,特别是当Mo在表面层上时.
发现HER的氧 p 波段中心 (ε_p) 和吸附能量 (ΔG(*H)) 之间存在强烈的相关性.
证明了地下金属原子通过连接体和应变效应影响表面催化活性.

结论:

扩大了潜在稳定的MXene化合物的范围,以便在未来进行合成和应用.
提供了对结构-属性关系的见解,这些关系决定了MXene对HER的催化性能.
强调了高的MXenes作为可持续生产的先进催化剂的潜力.

关键词:

积极学习是积极学习.密度函数理论密度函数理论她的催化剂是她的催化剂高透的MXenesenes是一种MXenes.的进化反应反应反应.神经网络潜力神经网络潜力

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