机器学习势在含能材料分子模拟中的研究进展

Recent Progress toward Molecular Modeling of Energetic Materials by Using Machine Learning Potential

归纳了机器学习势模型的发展历程、构造方案和训练集搭建策略。机器学习势模型利用第一性原理计算精度的势能面,通过机器学习算法进行重建,已成功用于含能材料燃烧爆炸的分子模拟研究,包括硝胺类含能材料(RDX、CL-20、ICM-102)、氧化剂(AP)和高能颗粒(Al、B)等,并总结了机器学习势在碳氢燃料燃烧方面的研究进展,展望了机器学习势在含能材料分子模拟中所面临的挑战和未来发展前景。指出以深度势(Deep Potential)模型为代表的机器学习势具有高精度、高效率的特点,充分发挥了基于数据驱动的模型训练策略,在保持第一性原理计算精度的同时,可以实现百万原子的分子模拟,具有广阔的应用潜力。提出未来含能材料机器学习势函数的开发将面临如下挑战:(1)如何对极端条件下复杂反应势面进行充分采样;(2)如何提高机器学习势训练集的精度。附参考文献91篇。

A comprehensive review was conducted on the historical development,construction scheme,and training strategy of the machine learning potential.This novel technique approximates the potential energy surface of molecular system at the level of first-principle calculations and has been successfully applied in the molecular modelling of combustion and explosion for energetic materials,including nitramine compounds(RDX,CL-20,and ICM-102),oxidizers(AP)and high-energy particles(Al,B).In addition,the representative applications of machine learning potential on the combustion of hydrocarbon fuels were also introduced.Furthermore,the challenges and future development perspectives of machine learning potential in energetic materials were discussed.It is well demonstrated that machine learning potentials,particularly deep potential models,are highly accurate and efficient.The data-driven approach makes it feasible to empower the simulation of million atoms with a great accuracy as first-principle calculations.In the final remark,the key challenges for the further development of machine learning potentials are discussed:(1)sampling issue for a complex potential energy surface under extreme conditions;(2)accuracy problem in the training dataset.With 91 references.