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.
By Anabel Yong, Matthew Hart, Philippe Schwaller
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.
By Guobin Zhao, Xiao-Yan Li
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.
By Kyungmin Nam, Seunghee Han, Jihan Kim
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.
By Mianzhi Pan, JianFei Li, Peishuo Liu, Botian Wang, Yawen Ouyang, Yiming Rong, Hao Zhou, Jianbing Zhang
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.
By Donghyun Lee, Taehoon Lee, Geonhee Ahn, Jieun Kim, Jihyun Park, Suyeon Cho, Yoona Kim, Chaerim Shin, Hoi Ri Moon, Jonggeol Na, Sukho Hong, Jihwan Oh, Soo Kyung Kim
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.
By Peter Walther, Hongrui Sheng, Xinxin Liu, Bin Feng, Reid Coyle, Xinhua Yan, Kyle Smith, Harrison Kayal, Shyam Chand Pal, Zhiling Zheng
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.
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.
By Chen Yuntong, Huang Ju, Liu Yu, Zhao Dan, Sun Mingqi, Ju Chentian, Liu Yanbing, Huang Lijiang, Zhao Guobin
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.
By Zhaolin Hu, Hehe Fan, Wangyihan Guo, Meng Xu, Chenhao Rao, Qiwei Yang, Yi Yang
arXiv:2607. 07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization.
By Chen Tang, Yizhou Wang, Jianyu Wu, Lintao Wang, Shixiang Tang, Pengze Li, Encheng Su, Jun Yao, Jiabei Xiao, Yuqi Shi, Jielan Li, Hongxia Hao, Zhangyang Gao, Fang Wu, Ben Fei, Xiangyu Yue, Pan Tan, Bozitao Zhong, Jinouwen Zhang, Aoran Wang, Yan Lu, Jiaheng Liu, Xinzhu Ma, Liang Hong, Mingyue Zheng, Phil Torr, Bowen Zhou, Wanli Ouyang, Lei Bai
Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization. Mechanistically explaining these relationships requires interpreting structural evidence through scientific principles and physical constraints, from stereochemistry and bonding to symmetry, energetics and periodic order.
The paper introduces a chemical language foundation model that uses a SMILES‑based polymer graph representation (CPG) to encode polymer architecture and connectivity, addressing gaps in existing line notations. The model achieves strong performance across 30 polymer property benchmarks and demonstrates robustness to structural representation perturbations, with even chemically invalid SMILES sometimes matching state‑of‑the‑art results. Control experiments and attention analyses confirm that CPG offers meaningful advantages while highlighting the model’s ability to interpolate SMILES sequence space in a way that aligns loosely with chemical and architectural space.
By Nathaniel H. Park, Eduardo Soares, Victor Y. Shirasuna, Tiffany J. Callahan, Sara Capponi, Emilio Vital Brazil