RaspAssistant is a hierarchical multi‑agent framework designed to automate the entire workflow of adsorption simulation research in porous materials, from force‑field preparation to validation and downstream separation analysis. In twelve benchmark runs across four adsorption and separation tasks, the system achieved overall scores between 8.50 and 10.00 on a rubric assessing workflow correctness, simulation validity, and analysis consistency. Additional tests on force‑field extraction and open‑ended studies demonstrated the framework’s ability to diagnose, refine, and validate force fields, as well as to screen large zeolite libraries automatically.
arXiv:2512. 19458v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly embedded in agentic frameworks for scientific discovery.
arXiv:2606. 19152v1 Announce Type: cross Abstract: Identifying the lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis, yet exhaustive exploration with ab initio calculations is computationally prohibitive.
arXiv:2607. 10559v1 Announce Type: new Abstract: Metal-organic frameworks (MOFs) offer a highly modular platform for adsorptive gas separation, yet their vast reticular design space makes inverse design difficult under simultaneous constraints of chemical validity, separation performance, and structural diversity.
Identifying the lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis, yet exhaustive exploration with ab initio calculations is computationally prohibitive. Machine-learning force fields (MLFFs) accelerate structural relaxation but leave the search over the vast configurational space a major bottleneck, and open-loop large language model (LLM) agents lack a physics-grounded feedback mechanism to correct erroneous initial guesses.
The study examined sixteen independent runs of a single autonomous materials discovery model, each given the same database of 12,499 metal‑organic frameworks and tasked with optimizing methane storage. The agents’ strategies varied widely, screening between 100 and 5,000 structures and constructing 2,253 hypothetical frameworks, yet all converged on the same high‑performance materials frontier near 200 cm³ cm⁻³. Even when half the agents were subjected to stricter checks, the overall conclusions remained unchanged, revealing both robust findings and a shared error stemming from an incomplete database entry.
CompMat-Bench is a new benchmark comprising 94 tasks drawn from recent computational materials science studies, designed to evaluate AI agents on realistic research steps without requiring costly simulations during testing. The benchmark pre‑reproduces inputs and outputs to provide ground truth, allowing agents to be graded with fixed rules rather than an LLM judge. It supports both single tasks and multi‑step workflows, with varying levels of methodological guidance, and shows that while agents can achieve high pass rates on individual tasks, performance drops in longer workflows or with reduced guidance, often due to scientific rather than software errors.
arXiv:2608. 07637v1 Announce Type: new Abstract: Long-running molecular simulation campaigns require repeated continuation from saved states, provenance-aware progression, adaptive assessment, and occasional interpretation of workflow conditions that cannot be resolved safely by fixed rules.
Metal-organic frameworks (MOFs) offer a highly modular platform for adsorptive gas separation, yet their vast reticular design space makes inverse design difficult under simultaneous constraints of chemical validity, separation performance, and structural diversity. Here, we present LEMO Agent, a large-language-model agent framework for closed-loop inverse design of gas-separation MOFs in MOFid space.
arXiv:2507. 14267v2 Announce Type: replace Abstract: Large language model (LLM) agents can execute long-horizon scientific workflows, but their numerical outputs are difficult to trust: agents lose context, game verification checks, and can produce large volumes of plausible yet invalid results.
arXiv:2609.23360v1 Announce Type: cross Abstract: Engineering predictions require physical mechanisms to be translated consistently into equations, discretization, code, and validation, yet errors ca...
arXiv:2608. 15776v1 Announce Type: cross Abstract: Despite the powerful multi-scale modeling methods and high-throughput infrastructures established in the materials community, real material computation workflows remain fragmented and heavily manual, requiring researchers to constantly bridge software tools, data analysis, and intermediate decisions.
MatOS is an agent operating system designed to streamline materials research by organizing data, knowledge, tools, and workflows into reusable assets. It features a layered scientific asset graph, a stateful adaptive workflow that tracks and resumes tasks, and an evidence-driven asset evolution mechanism that updates assets based on execution records and user feedback. In benchmark tests, MatOS outperforms existing agents and has successfully narrowed millions of candidate structures to a few promising solid-electrolyte and halide variants, demonstrating its effectiveness in long, multistage materials studies.