A High-Dimensional Neural Network Potential for Co$_3$O$_4$

Amir Omranpour, Jörg Behler
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Abstract

The Co$_3$O$_4$ spinel is an important material in oxidation catalysis. Its properties under catalytic conditions, i.e., at finite temperatures, can be studied by molecular dynamics simulations, which critically depend on an accurate description of the atomic interactions. Due to the high complexity of Co$_3$O$_4$, which is related to the presence of multiple oxidation states of the cobalt ions, to date \textit{ab initio} methods have been essentially the only way to reliably capture the underlying potential energy surface, while more efficient atomistic potentials are very challenging to construct. Consequently, the accessible length and time scales of computer simulations of systems containing Co$_3$O$_4$ are still severely limited. Rapid advances in the development of modern machine learning potentials (MLPs) trained on electronic structure data now make it possible to bridge this gap. In this work, we employ a high-dimensional neural network potential (HDNNP) to construct a MLP for bulk Co$_3$O$_4$ spinel based on density functional theory calculations. After a careful validation of the potential, we compute various structural, vibrational, and dynamical properties of the Co$_3$O$_4$ spinel with a particular focus on its temperature-dependent behavior, including the thermal expansion coefficient.
Co$_3$O$_4$ 的高维神经网络潜力
Co$_3$O$_4$ 尖晶石是一种重要的氧化催化材料。它在催化条件下(即在有限温度下)的特性可通过分子动力学模拟进行研究,而分子动力学模拟关键取决于对原子相互作用的精确描述。由于 Co$_3$O$_4$ 的高度复杂性(这与钴离子存在多种氧化态有关),迄今为止,textit{ab initio} 方法基本上是可靠捕捉潜在势能面的唯一方法,而构建更有效的原子势能面则非常具有挑战性。以电子结构数据为基础训练的现代机器学习势能(MLP)的发展突飞猛进,使得弥合这一差距成为可能。在本研究中,我们基于密度泛函理论计算,采用高维神经网络势(HDNNP)为块状 Co$_3$O$_4$ 尖晶石构建了一个 MLP。在对该势能进行仔细验证后,我们计算了 Co$_3$O$_4$ 尖晶石的各种结构、振动和动力学特性,并特别关注其随温度变化的行为,包括热膨胀系数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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