Machine Learning-assisted Discovery of Multifunctional Coordination in Multicomponent Composites
简介：The complex interactions and conflicting performance demands in multi-component composites pose significant challenges for achieving balanced multi-property optimization through conventional trial-and-error approaches.Machine learning(ML)offers a promising solu-tion,markedly improving materials discovery efficiency.However,the high dimensionality of feature spaces in such systems has long impeded ef-fective ML-driven feature representation and inverse design.To overcome this,we present an Intelligent Screening System(ISS)framework to ac-celerate the discovery of optimal formulations balancing four key properties in 15-component PTFE-based copper-clad laminate composites(PTFE-CCLCs).ISS adopts modular descriptors based on the physical information of component volume fractions,thereby simplifying the feature representation.By leveraging the inverse prediction capability of ML models and constructing a performance-driven virtual candidate database,ISS significantly reduced the computational complexity associated with high-dimensional spaces.Experimental validation confirmed that ISS-optimized formulations exhibited superior synergy,notably resolving the trade-off between thermal conductivity and peel strength,and outper-form many commercial counterparts.Despite limited data and inherent process variability,ISS achieved an average prediction accuracy of 76.5％,with thermal conductivity predictions exceeding 90％,demonstrating robust reliability.This work provides an innovative,efficient strategy for multifunctional optimization and accelerated discovery in ultra-complex composite systems,highlighting the integration of ML and advanced materials design.展开
学者：Zi-RanGUOSenXueLuHeZi-LongXieTian-HaoYangQiangFu
关键词：Multicomponent CompositesMachine learningMulti-performance trade-offThermal conductivityAdhesive property
在线出版日期：2026-04-03 （网站首发日期）
