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.

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

The paper proposes a novel multimodal learning approach to predict the properties of new bilayer 2D materials formed by stacking dissimilar functional layers.

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 introduces GeoCoupling, a framework that systematically optimizes the temporal coupling between heterogeneous modalities to improve the co-design of biomolecules, outperforming fixed synchro…

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…

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

MolLingo is a multi-agent system that significantly improves automated molecular design by integrating domain-specific chemical reasoning and structural context into LLMs, outperforming state-of-the-a…

The paper proposes a category-theoretic framework for agentic AI that models scientific discovery not as answer generation, but as a verifiable transition and revision of the underlying representation…

The paper introduces FTDiff, a reinforcement learning fine-tuning framework that efficiently generates high-quality, drug-like molecules constrained by a target protein structure, outperforming existi…

The paper introduces Battery-Sim-Agent, an LLM-based framework that reframes the difficult inverse problem of battery parameter estimation as a reasoning task, significantly outperforming traditional…

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…

The paper introduces a unified Physics-Informed Deep Learning (PIDL) framework that simultaneously enforces physical laws and information-theoretic bounds, demonstrating robust, domain-agnostic entrop…

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 introduces an operator-level factorial benchmark for molecular MPNNs, finding that message construction (specifically concatenation-based mixing) is the primary determinant of performance, r…