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

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 '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 DOMINO, a novel inductive framework that synthesizes domain-specific data for LLMs using only reference examples, significantly improving performance on challenging, implicitly de…

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

EvoMD-LLM introduces a novel framework that models reactive molecular dynamics as a symbolic temporal language problem, enabling LLMs to accurately predict complex, time-evolving chemical processes.

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…

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

The paper introduces ProjectionBench, a novel benchmark that progressively discloses information to evaluate LLMs' ability to generate scientific hypotheses, demonstrating that advanced models like GP…

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

The paper introduces StreamSynth, a sequential setting for synthetic data generation, and proposes SynLearner, a framework that enables LLMs to improve synthesis performance by accumulating and transf…

The paper proposes a novel information-geometric framework to analyze LLM stability by integrating task utility, external entropy, and internal structural proxies, showing this composite score improve…

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 an objective-wise reputation-market mechanism to dynamically calibrate and gate LLM-generated expert priors in multi-objective Bayesian optimization, showing that dynamic calibratio…