2026· Poster Volume 0008 The 2026 Twenty-Second International Conference on Intelligent Computing July 23-26, 2026 Toronto, Canada· pp. 450-479· 0 citations
TL;DR
An entropy-aware and representation-driven paradigm for the intelli-gent design of soft functional materials is established, offering a generalizable pathway for accelerating discovery and enhancing extrapolation capability.
Abstract
The rapid advancement of artificial intelligence (AI) and machine learning (ML) is reshaping scientific discovery through an information-theoretic par-adigm. Modern large-scale models function as entropy-compression engines: they extract low-entropy, transferable representations from high-dimensional and noisy data, reducing epistemic uncertainty in complex physical systems. This perspective motivates growing interest in entropy- oriented and infor-mation-theoretic approaches to model compression, generalization, and ro-bustness. Soft matter materials—particularly multiscale and compositionally diverse hydrogels—naturally constitute a high-entropy design landscape. Traditional intuition-driven exploration struggles to navigate such spaces or capture nonlinear, cross-scale dependencies reminiscent of complex-system behaviors. To address this challenge, we develop an intelligent hydrogel de-sign framework grounded in information theory and multiscale representa-tion learning. Feature engineering acts as an entropy-reduction step, com-pressing raw variables into structured representations that support stable multi-task learning. A multi-objective Bayesian optimization scheme with Gaussian-process surrogates and a q-Expected Hypervolume Improvement (q-EHVI) acquisition function further performs targeted information acquisi-tion, maximizing posterior entropy reduction while efficiently expanding the Pareto front. A self-evolving feedback loop integrates AI prediction with ex-perimental validation, allowing each iteration to reduce system-level uncer-tainty and refine representations across scales. Incorporating entropy-based and chaos-inspired metrics provides additional diagnostics for robustness and sensitivity, aligning the workflow with emerging information-theoretic prin-ciples used in reliable large-model development. Overall, this study estab-lishes an entropy-aware and representation-driven paradigm for the intelli-gent design of soft functional materials, offering a generalizable pathway for accelerating discovery and enhancing extrapolation capability.
Mechanistic simulation and machine learning are powerful but complementary tools: physics-based simulation is interpretable yet computationally expensive and blind to real-world context, whereas machine learning is fast but data-hungry and opaque. Biological systems resolve this tension elegantly, coupling physically grounded mechanoreceptor sensing with higher-level neural interpretation that places those signals in context. Inspired by this layered architecture, we present Mechanics-AI, an open-source framework that mirrors the same sensing-then-interpretation logic computationally. A first learning layer (ML1) emulates expensive finite-element, computational fluid dynamics and multiphysics simulations to produce interpretable physical metrics such as stress, strain, shear, and thermal and moisture fields, while a second layer (ML2) fuses these metrics with heterogeneous real-world metadata to predict categorical outcomes and design recommendations. Eight algorithms are benchmarked automatically, the most accurate is selected for each task, and Shapley additive explanations expose the dominant physical drivers to preserve interpretability. The framework is demonstrated across three independent domains using a single unchanged pipeline: forensic traumatic brain injury prediction, optimisation of a bio-inspired humanoid bioreactor for tissue engineering, and a zero-emission building (ZEBAI) framework that couples thermo-hygro-mechanical simulation with Sobol-sampled surrogate modelling to design sustainable, low-carbon envelopes from recycled aggregate concrete by balancing structural safety, energy and embodied carbon. Despite entirely different physics, data and objectives, the same architecture generalises across all three, showing that bio-inspired, layered coupling of mechanistic simulation and contextual learning offers a reusable, interpretable route to cross-domain engineering prediction and sustainable design.
Yuyang Wei, Weijie Fei, Jiarong Wang et al.· Biomimetics· 0 citations
Polymeric materials are central to modern technologies, with applications ranging from energy to health and transportation. Although AI has made significant advances in materials discovery, the hierarchical structure of polymers across multiple length scales makes them inherently difficult to represent in a unified and physically meaningful way. Here we introduce HiPoly, a polymer-native AI framework that processes complete polymer descriptions through a three-level hierarchical graph architecture built on the G2RINS representation. HiPoly encodes stochastic inter-monomer connectivity, composition, and molecular weight directly within its architecture, using physically motivated design principles that mirror the multi-scale nature of polymeric systems. The framework establishes an end-to-end AI-driven workflow from experimental formulation data to property prediction, generative molecular design, and physics-based validation through molecular simulations, all unified by a single polymer representation. We demonstrate state-of-the-art prediction accuracy for thermophysical properties of multi-component polymer systems, with ablation studies confirming that each hierarchical design choice contributes independently to model performance. As an example, the generative design pathway is applied here to the discovery of sustainable alternatives to persistent fluorinated polymers, where it is possible to identify and independently validate PFAS-free candidates with target surface-energy properties. This work demonstrates how polymer-native AI can accelerate discovery by linking representation, prediction, and design across complex polymer chemistries.
Ge Sun, Gervasio Zaldivar, Yuan Tian et al.· 0 citations
The discovery of advanced alloys capable of withstanding extreme environmental conditions such as high temperatures, intense radiation, corrosive atmospheres, and mechanical stress is critical for applications in aerospace, nuclear energy, deep-sea exploration, and space missions. Traditional experimental and computational approaches to alloy design are often time-consuming and resource-intensive. Recent advances in artificial intelligence (AI) and machine learning (ML) offer powerful tools to accelerate the discovery process by enabling high-throughput screening, property prediction, and design optimization. This paper presents a comprehensive review and methodology for AI-assisted alloy discovery, focusing on the integration of data-driven models with physical principles, high-fidelity simulations, and experimental validation. We highlight successful case studies, discuss the challenges of data scarcity and model interpretability, and propose a framework for closed-loop design that incorporates generative models and active learning. This AI-driven approach represents a paradigm shift toward faster, more cost-effective discovery of next-generation materials for extreme environments.
Venkatesh Iyer, Nandhini Ravi· International Journal of Mod...· 0 citations
Continued efforts in mechanism-driven descriptor design, multi-level modeling, and the integration of computational and experimental data are expected to enable the rational design of next-generation Pt alloys with tailored mechanical properties.
W. Lu, Lan-Yu Zhang, Jing Lu et al.· NANO· 0 citations
An artificial intelligence framework that incorporates a physics-constrained inverse-design system (PHICS) built upon a directed acyclic graph (DAG) architecture for CPCMs, integrating interface, phase, and carrier engineering.
Dongliang Ding, Min-hao Zou, Ruoyu Huang et al.· National Science Review· 0 citations
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