The paper introduces OmniMatBench, a comprehensive, human-calibrated multimodal reasoning benchmark covering 19 materials science subfields, revealing that current multimodal language models (MLLMs) h…

This review surveys advanced techniques—including generative models, multimodal learning, and closed-loop workflows—for automated inverse materials design, enabling the targeted discovery of novel cry…

The paper proposes a novel hybrid framework that couples Large Language Models (LLMs) with simplified physics-based simulations to improve the synthesis planning of novel inorganic crystalline materia…

The paper introduces a novel padding method that leverages crystal symmetry to enhance the encoding of complex inorganic structures, significantly improving the generation of stable, novel materials.

This paper develops a supervised machine learning surrogate model, using a neural network, to predict the effective Lamé parameters of hyperelastic composites based on low-dimensional microstructural…

The paper introduces an improved PULSE method to efficiently estimate the thermodynamic properties of chemically disordered compounds by sampling and estimating the system's partition function, demons…

GaMi is a multimodal material identification system that uses mmWave and acoustic sensing with a cross-modal subtractive disentanglement framework to achieve high accuracy (95.2%) for material identif…

This perspective reviews advanced design principles for Metal-Organic Frameworks (MOFs) used in water harvesting and details how integrating Artificial Intelligence (AI) can accelerate the discovery o…

The paper proposes a graph attention-based virtual metrology framework that accurately predicts film thickness in semiconductor deposition by modeling structured, directional dependencies among hetero…

The paper introduces a physics-informed active learning framework to optimize GaN tri-gate FinFETs for vertical power delivery, identifying a multi-fin device (D1) that significantly outperforms a sin…

The paper introduces CrystalXRD-Bench, a new benchmark designed to test Vision-Language Models (VLMs) on the complex task of identifying crystallographic Miller indices (HKLs) from rendered X-ray Diff…

The paper proposes MITL, an MsFEM-inspired transfer learning strategy for CNN-based reduced-order models, enabling efficient and adaptable approximation of multiscale systems with minimal retraining.

The paper introduces 'Matter to Mechanism,' a novel benchmark designed to rigorously evaluate AI co-scientists' ability to generate plausible, mechanism-grounded solution hypotheses for complex materi…

The paper explains the 'table-chart gap' in scientific claim verification by showing that multimodal LLMs successfully encode information from charts but fail to route it to the final prediction layer…

The paper introduces an integrated computational toolbox using topological and fractal analysis to quantitatively track microstructural changes during casein gelation, correlating these subtle changes…

The paper introduces ProvMind, a provenance-grounded reasoning framework that significantly improves materials synthesis process optimization by accurately predicting optimal synthesis routes under ch…

This study empirically benchmarks classical and quantum machine learning models for image recognition, finding that while quantum models offer superior accuracy and resource efficiency at high dimensi…

This paper presents a unified framework for end-to-end co-design of neural network processors.