Mechanochemical and machine-intelligent design of programmable materials: From molecular interactions to macroscale functionality

Programmable materials are an emerging class of matter capable of dynamically altering their properties, structure, or function in response to external stimuli. While most research has treated chemical and mechanical responsiveness separately, integrating these domains through mechanochemical design opens new avenues for intelligent, adaptive systems. This review explores how chemical reactivity and molecular interactions can be harnessed alongside mechanical deformation to create materials with controllable behavior across multiple scales. Key topics include force-activated molecular units (mechanophores), stress-guided chemical patterning, and materials whose structure-function relationships evolve under load. We highlight the role of machine intelligence in accelerating the discovery and optimization of programmable metamaterials, emphasizing inverse design, data-driven property prediction, and autonomous adaptation. Applications in soft robotics, shape-memory systems, self-healing materials, and smart coatings are discussed, focusing on chemomechanical feedback loops enhanced by computational tools. Multiscale modeling approaches that integrate chemical kinetics, mechanical stress analysis, and AI-guided generative design are also reviewed. By bridging polymer science, molecular chemistry, mechanical engineering, and artificial intelligence, this framework enables the design of materials that are not only responsive but predictive and self-evolving. Current challenges including scalability, reversibility, and durability are considered, alongside future directions toward biologically inspired, resilient material systems. • Introduces a unified mechanochemical design framework integrating chemical reactivity with mechanical deformation. • Reviews advances in force-activated molecular units (mechanophores) and stress-guided chemical patterning. • Explores evolving structure–function relationships in load-adaptive programmable materials. • Examines AI-driven inverse design, multiscale modeling, and data-enabled optimization of metamaterials. • Discusses applications in soft robotics, shape-memory systems, self-healing materials, and smart coatings. • Identifies key challenges including scalability, reversibility, durability, and autonomous adaptation.