Faraji Somayeh, Liu Mingjie
Department of Chemistry, University of Florida, Gainesville, FL 32611, USA.
Phys Chem Chem Phys. 2024 Aug 28;26(34):22346-22358. doi: 10.1039/d4cp02300e.
In this study, we developed a machine learning interatomic potential based on artificial neural networks (ANN) to model carbon-hydrogen (C-H) systems. The ANN potential was trained on a dataset of C-H clusters obtained through density functional theory (DFT) calculations. Through comprehensive evaluations against DFT results, including predictions of geometries and formation energies across 0D-3D systems comprising C and C-H, as well as modeling various chemical processes, the ANN potential demonstrated exceptional accuracy and transferability. Its capability to accurately predict lattice dynamics, crucial for stability assessment in crystal structure prediction, was also verified through phonon dispersion analysis. Notably, its accuracy and computational efficiency in calculating force constants facilitated the exploration of complex energy landscapes, leading to the discovery of a novel C polymorph. These results underscore the robustness and versatility of the ANN potential, highlighting its efficacy in advancing computational materials science by conducting precise atomistic simulations on a wide range of C-H materials.
在本研究中，我们基于人工神经网络（ANN）开发了一种机器学习原子间势，用于对碳氢（C-H）系统进行建模。该ANN势在通过密度泛函理论（DFT）计算获得的C-H团簇数据集上进行训练。通过与DFT结果进行全面评估，包括对包含C和C-H的0D - 3D系统的几何结构和形成能的预测，以及对各种化学过程的建模，该ANN势展现出了卓越的准确性和可转移性。通过声子色散分析，还验证了其准确预测晶格动力学的能力，这对于晶体结构预测中的稳定性评估至关重要。值得注意的是，其在计算力常数方面的准确性和计算效率有助于探索复杂的能量景观，从而发现了一种新型的C多晶型物。这些结果强调了ANN势的稳健性和通用性，突出了其通过对广泛的C-H材料进行精确的原子模拟，在推进计算材料科学方面的功效。
