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This paper presents an agent-based system within the URSA framework that autonomously designs, executes, and validates atomistic simulations using LAMMPS, reducing manual intervention and improving reproducibility.
Researchers demonstrate how origami folds can create conductive traces in liquid-metal-impregnated paper, enabling hybrid papercraft objects with integrated electronics.
This paper presents AlphaAgent, a skill-driven agent framework for evidence-aware materials literature analysis, which decouples retrieval-based QA from report generation and outperforms a baseline on 40 materials-science questions.
Researchers at the University of Chicago and collaborators have developed a cleaner method to separate rare earth elements using electrochemical intercalation in layered manganese oxide, avoiding toxic solvents. The study was published in Nature Chemical Engineering.
Researchers developed a small-molecule glue, CyclicFP-fmoc, that bonds strongly to nonstick Teflon surfaces via fluorine-fluorine interactions and can be easily removed with ethanol, enabling recycling and reuse. The adhesive achieves high strength and ductility, outperforming commercial epoxies, and leaves no residue upon ethanol wash.
This paper demonstrates that materials science mechanisms in the open-weight Gemma model can be identified and controlled through hidden state analysis, causal interventions, and counterfactual benchmarks, revealing that physical relationships are more visible in state transformations than in absolute states.
Advanced materials are critical for enabling next-generation AI by addressing performance challenges in semiconductor manufacturing and data center infrastructure. The article highlights how materials innovation in polymers, fluids, and thermal management supports the physical demands of increasingly powerful AI systems.
Metal-organic frameworks (MOFs) are a revolutionary class of porous crystalline solids with applications in carbon capture, water harvesting from desert air, and drug delivery. The article highlights the work of Berkeley chemist Omar Yaghi and the potential of MOFs to address environmental and energy challenges.
Proposes a novel multilevel graph prompt learning framework for crystal property prediction that captures local chemical semantics and global structural symmetry via node-level and graph-level soft prompts, improving state-of-the-art GNN performance by 3%-15% and enabling cross-property knowledge transfer.
This paper introduces the Hypothesis Evolution Protocol (HEP) for LLM agents, which makes hypothesis generation, testing, and belief updates explicit and auditable. Experiments on materials-science tasks show that HEP-equipped agents generalize across research questions and become more effective with stronger base LLMs.
Researchers discovered that simple, nonelastic liquids can undergo brittle fracture under extensional stress, contradicting prior assumptions that elasticity was necessary for such behavior.
Atomscale argues that materials innovation faces a scale-up bottleneck rather than a discovery problem, using examples from semiconductor manufacturing to illustrate the challenge of moving materials from lab to production.
Researchers developed a new method to create a 'super alloy' by controlling atomic organization at lower temperatures, resulting in a metal that is twice as strong as steel and three times as strong as aluminum, potentially transforming metal manufacturing.
Damo Academy (Alibaba) introduces Elements Claw, an AI agent that discovered four new superconducting materials by screening millions of crystal structures, potentially accelerating materials research.
Google Cloud will offer SandboxAQ's large quantitative models (trained on scientific equations and lab data) for drug discovery, materials science, and semiconductor manufacturing, alongside Gemini for Science tools to accelerate research workflows.
Introduces MatMMExtract pipeline to decompose compound scientific figures into panels and annotate them using LLMs, creating the MatSciFig dataset of over 390,000 image-text pairs for vision-language learning in materials science.
The Nanotechnology Molecular Optimization (NMO) Benchmark introduces physics-based molecular design tasks replacing drug-discovery-focused metrics, aiming to drive scientific discovery in nanotechnology. The paper shows that advanced methods underperform simpler approaches on NMO, and proposes new baseline methods including a novel representation and domain-agnostic pretraining.
This paper introduces an atomistic language model that integrates a 3D atom encoder, Qwen LLM, and diffusion crystal generator to natively handle multimodal materials data, achieving state-of-the-art crystal structure prediction and de novo generation.
NVIDIA announces new AI software libraries and microservices—DAQIRI, ALCHEMI, and cuPhoton—that dramatically accelerate scientific workloads in fields like astronomy, materials science, and particle physics, achieving up to 14,900x speedups over CPU-based pipelines.
MIT researchers have developed a machine-learning-based approach to accurately model the behavior of metal alloys, regardless of chemical complexity, enabling faster and cheaper materials innovation.