Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential

Rina Ibragimova, Mikhail S. Kuklin, Tigany Zarrouk, Miguel A. Caro
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Abstract

We present a general-purpose machine learning (ML) interatomic potential for carbon and hydrogen which is capable of simulating various materials and molecules composed of these elements. This ML interatomic potential is trained using the Gaussian approximation potential (GAP) framework and an extensive dataset of C-H configurations obtained from density functional theory. The dataset is constructed through iterative training and structure-search techniques that generate a broad range of configurations to comprehensively sample the potential energy surface. Furthermore, the dataset is supplemented with relevant bulk, molecular, and high-pressure structures. Finally, long-range van der Waals interactions are added as a locally parametrized model. The accuracy and generality of the potential are validated through the analysis of different simulations under a wide range of conditions, including weak interactions, high temperature, and high pressure. We show that our CH GAP model describes different problems such as the formation of simple and complex alkanes, aromatic hydrocarbons, hydrogenated amorphous carbon (a-C:H), and CH systems at extreme conditions, while retaining good accuracy for pure carbon materials. We use this model to generate hydrocarbons of different sizes and complexity without prior knowledge of organic chemistry rules, and to highlight intrinsic limitations to the simultaneous description on intra and intermolecular interactions within a single computational framework. Our general-purpose ML interatomic potential has the capability to significantly advance research in the field of H-containing carbon materials and compounds, particularly in the areas where longer dynamics, reactivity and large-scale effects may be important.
用化学反应机器学习原子间势统一描述碳氢化合物和氢化碳材料
我们提出了一种通用的机器学习(ML)碳氢原子间势,它能够模拟由这些元素组成的各种材料和分子。这种 ML 原子间位势是利用高斯近似位势(GAP)框架和从密度泛函理论获得的 C-H 构型扩展数据集进行训练的。该数据集是通过迭代训练和结构搜索技术构建的,可生成广泛的构型,从而对势能面进行全面采样。此外,数据集还补充了相关的块体、分子和高压结构。最后,长程范德瓦耳斯相互作用被添加为局部参数化模型。通过分析各种条件下的模拟(包括弱相互作用、高温和高压),我们验证了该势垒的准确性和通用性。我们的研究表明,我们的 CH GAP 模型可以描述不同的问题,例如在极端条件下简单和复杂烷烃、芳香烃、氢化无定形碳(a-C:H)和 CH 系统的形成,同时对纯碳材料保持良好的准确性。我们利用该模型生成了不同大小和复杂程度的碳氢化合物,而无需事先了解有机化学规则,并强调了在单一计算框架内同时描述分子内和分子间相互作用的内在局限性。我们的通用 ML 原子间势能极大地推动了含氢碳材料和化合物领域的研究,尤其是在长动力学、反应性和大尺度效应可能很重要的领域。
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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