高压物理学报2025，Vol.39Issue(11)：36-45,10.DOI:10.11858/gywlxb.20251188
可控加载功能梯度材料设计程序改进与高通量优化设计
Improvement of the Design Program for Functionally Graded Materials with Controllable Loading and High-Throughput Optimization Design
摘要
Abstract
To achieve high-throughput optimization design of gradient materials,it is essential to establish accurate and rapid predictive capabilities for the loading performance of such materials.The rapid advancement of artificial intelligence technology combined with hardware development has gradually become a revolutionary research tool across various scientific fields.In materials science,machine learning methods play a significant role in high-throughput material design and performance prediction.This study introduces machine learning methods into the optimization design of functionally graded materials with controllable loading.By integrating computational results from physics-based models,a relatively accurate rapid prediction model was established,significantly enhancing optimization throughput.The multi-material fluid-elastoplastic computational program MLEP has undergone multiple rounds of validation in the experimental design and data interpretation of gradient materials,demonstrating high predictive accuracy for experimental results.Numerical experimental samples based on this program can be used to construct high-precision surrogate models.To extend MLEP's applicability to a broader range of density-gradient material design and experimental prediction,the p-α model has been incorporated into the existing simulation framework.This model describes the mechanical behavior of low-density polymers under shock/quasi-isentropic loading,enabling the expansion of flyer plate density from approximately 0.5 g/cm3 to 15.0 g/cm3.关键词
功能梯度材料/机器学习/高通量设计Key words
functional gradient materials/machine learning/high-throughput optimization分类
数理科学引用本文复制引用
李蕾,陈翰,柏劲松,张睿智,张建,吴楯..可控加载功能梯度材料设计程序改进与高通量优化设计[J].高压物理学报,2025,39(11):36-45,10.基金项目
国家重点研发计划(2021YFB3802300) （2021YFB3802300）
