AI 中文总结
本研究以聚乙烯为模型，对比原子描述符确定ACE最有效，通过自动化主动学习生成训练数据，证明低聚物拟合的ACE势可迁移至大分子聚合物，且能准确再现相关性质。
AI 中文摘要
在过去十年中，机器学习原子间势（MLIPs）已成为开展近从头算精度的分子动力学（MD）模拟的强大技术。随着新描述符和先进机器学习架构的发展，多样化且准确的参考数据集生成的复杂流程已建立。迄今为止，研究主要聚焦于晶体或无定形无机及小分子体系的MLIPs；然而，尽管聚合物是重要的材料类别，大型大分子如聚合物在文献中仍被研究不足。本研究以聚乙烯作为具代表性但简单的模型体系，探究开发聚合物MLIPs的若干方面：首先，比较多种局部原子描述符，确定原子簇展开（ACE）为此应用最有效的方法；其次，实施并自动化主动学习方案以高效生成多样化训练数据，证明拟合于低聚物的ACE势可迁移至更大的聚合物。鉴于密度的准确再现关键依赖于对分子间相互作用的正确描述，而分子间相互作用比分子内相互作用更难学习，我们针对非键相互作用仔细评估ACE势的性能。通过使用计算高效的OPLS-AA力场作为基准参考，我们能够直接比较由ACE势和参考势产生的纳秒级MD轨迹。研究发现，ACE势可准确再现关键的热力学、结构和动力学性质。
英文摘要
Over the past decade, Machine Learning Interatomic Potentials (MLIPs) have emerged as a powerful technique for performing molecular dynamics (MD) simulations with nearly ab initio accuracy. Alongside the development of new descriptors and advanced machine learning architectures, sophisticated procedures for the generation of diverse and accurate reference datasets have been established. To date, research has focused primarily on MLIPs for crystalline or amorphous inorganic and small molecular systems; however, large macromolecules such as polymers remain underrepresented in the literature, despite beeing an important class of materials. In this work, we investigate several aspects of developing MLIPs for polymers, utilizing polyethylene as a representative, yet simple model system. First, we compare various local atomic descriptors, identifying the Atomic Cluster Expansion (ACE) as the most effective for this application. Second, we implement and automatized active learning scheme to efficiently generate diverse training data and demonstrate that ACE potentials fitted on small oligomers are transferable to larger polymers. Given that the accurate reproduction of the density depends critically on a correct description of intermolecular interactions, which are far more complex to learn than intramolecular interactions, we carefully evaluate the performance of the ACE potentials with respect to non-bonded interactions. By utilizing the computationally efficient OPLS-AA force field as a ground truth reference, we are able to perform a direct comparison of nanosecond-scale MD trajectories resulting from the ACE and reference potential. We find that the ACE potential accurately reproduces key thermodynamic, structural and dynamical properties.
CommentsMain manuscript: 21 pages, 8 figures; Supporting Information: 16 pages, 12 figures
