无机材料学报2026，Vol.41Issue(4)：455-461,中插3-中插4,9.DOI:10.15541/jim20250299
高熵硼化物陶瓷机器学习力场的构建与高温性能计算
Machine Learning Potential Development and High-temperature Property Calculation for High-entropy Boride Ceramics
摘要
Abstract
Molecular dynamics simulations of high-entropy boride ceramics(HEBCs)in extreme high-temperature environments are constrained by limited accuracy and temperature stability of empirical force fields.In this work,a high-accuracy deep-learning potential(DP)was proposed and developed for(Hf0.2Zr0.2Ta0.2Ti0.2Nb0.2)B2 systems via first-principles calculations and deep learning method.It is shown that,through expanding datasets via the active learning strategy,the DP model stability under high-temperature conditions(i.e.,～3000 K)could be significantly enhanced.The developed DP achieves high accuracy while maintaining computational efficiency.Validation results from the developed DP manifest that predictions of the volumetric equation of state align well with first-principles calculations,demonstrating the model's good scalability.The lattice constants and mechanical properties predicted by DP-enabled molecular dynamics simulations show excellent agreements with experimental observations,with relative errors within 2%.Furthermore,the simulations successfully reveal the anisotropic thermal expansion behavior of HEBCs and rectify the anomalous trends reported in previous research.Therefore,this developed DP model provides a reliable tool for atomic-scale simulations of high-entropy boride ceramics under extreme conditions,and holds significant scientific value for advancing the in-depth understanding of their high-temperature service behavior.关键词
高熵硼化物陶瓷/分子动力学/深度学习势能/高温性能Key words
high-entropy boride ceramic/molecular dynamics/deep-learning potential/high temperature property分类
通用工业技术引用本文复制引用
龚焕,张旭,张小锋,李蓓,刘凯..高熵硼化物陶瓷机器学习力场的构建与高温性能计算[J].无机材料学报,2026,41(4):455-461,中插3-中插4,9.基金项目
国家自然科学基金(52202066)National Natural Science Foundation of China(52202066) （52202066）
