Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

ABSTRACT Soft functional materials are essential for wearables and stretchable electronics to meet multiple performance demands. Hybrid filler composites (HFCs) with liquid and solid inclusions offer tailored properties. However, identifying optimal compositions through conventional trial‐and‐error is costly, inefficient, and generates substantial material waste. We present an inverse design framework for hybrid liquid metal composites that combines data generation from a physics‐based homogenization model with machine learning (ML) algorithms and Bayesian optimization to enable intelligent design of experiments. This framework explores ∼690 000 composite formulations across diverse polymers and solid fillers, revealing key composition–property relationships while achieving targeted optimization of thermal conductivity, elasticity, and density with minimal material use. Comparative studies between Random Forest regression and a generative model provide practical strategies for identifying synthesizable composites. An inversely designed HFC achieves a thermal conductivity of ∼2.4 W/(m·K), 1.6 × that of the liquid‐metal composite and 12 × that of the polymer, while maintaining low‐modulus, high‐strain mechanics (0.93 MPa, 155% strain) at reduced cost. Integration into flexible electronics and thermoelectrics demonstrates enhanced thermal management and energy harvesting. This work establishes a practical, ML‐assisted inverse design paradigm for guiding the discovery of multifunctional composites, readily extendable to other material systems and properties.