MAESTRO is a large language model agent that automates the full screening pipeline for metal‑organic frameworks (MOFs). It parses extensive MOF literature, links publications to crystal structures, curates a computation‑ready database, and then applies a progressively more expensive computational strategy to identify promising candidates. The identified materials for wet flue gas separation come from unrelated studies, demonstrating the agent’s ability to uncover high‑performance materials across domains.
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.
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:2606. 29459v1 Announce Type: cross Abstract: Inverse design of metal-organic frameworks (MOFs) requires searching a combinatorially vast space where property labels are expensive and most machine-learning models reveal little about why a structure succeeds.
arXiv:2608. 11283v1 Announce Type: cross Abstract: Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disordered, compromising simulation fidelity.
arXiv:2604. 20899v2 Announce Type: replace-cross Abstract: Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports.
MOF-VERIFY is a failure-aware agentic harness designed to improve hypothesis verification for metal‑organic frameworks (MOFs). It introduces a diagnostic benchmark with four task families—structural grounding, synthesis‑condition verification, evidence‑sufficiency verification, and MLIP‑based computational verification—to pinpoint failures in knowledge access, evidence acquisition, and reasoning. Guided by these diagnostics, MOF‑Verify addresses structural, literature, evidence‑sufficiency, and computational bottlenecks, achieving significant performance gains over direct inference and retrieval‑based baselines across multiple large language models.
arXiv:2601. 09285v2 Announce Type: replace Abstract: Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge.
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.
arXiv:2610.19404v1 Announce Type: cross Abstract: Reticular chemistry allows metal-organic frameworks (MOFs) to be designed from their building units, yet their synthesis remains constrained by spars...
Language models for metal-organic frameworks (MOFs) rely on formats that separate atomic geometry from structural topology, forcing models to choose between unstructured coordinate lists and non-spati...
MOFjson is a new representation for metal‑organic frameworks that separates structural data into a raw atomic layer with explicit bond shift vectors and a derived layer that exposes secondary building units and network topology. The authors benchmarked MOFjson against three baselines across four question categories, achieving superior performance (e.g., 100% vs. 18‑47% for nearest periodic copy detection). Ablation of shift labels reduced accuracy dramatically, demonstrating that models rely on the periodic graph rather than surface format. MOFjson provides language models with a structured, topological view of MOFs, enabling more accurate predictions and facilitating agentic tool workflows and constrained MOF generation.
The paper introduces SciKGExtract, a schema-guided framework that uses large‑language‑model extraction, chemical normalization, and agent‑based evaluation to convert heterogeneous materials science literature into a knowledge graph. Applied to 176 atomic‑layer‑deposition papers on ZnO and IGZO, the system improves extraction F1 from 0.591 to 0.805 for ZnO with agentic refinement, while IGZO remains more challenging at 0.344. Evaluation against a detailed schema of 65 experimental properties and 155 quantitative nodes reveals segmentation and numerical assignment errors, highlighting the complementary role of chemical canonicalization and agentic verification in producing machine‑actionable experimental knowledge.