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: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: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: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
arXiv:2605. 29179v2 Announce Type: replace-cross Abstract: Metal-organic frameworks (MOFs) are excellent candidates for water harvesting due to their tunable pore environments, which can be precisely engineered to capture and release water in arid conditions.
By Reid A. Coyle, Shyam Chand Pal, Peter Walther, Saeun Park, Bin Feng, Zhiling Zheng
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.