LEMO Agent is presented, a large-language-model agent framework for closed-loop inverse design of gas-separation MOFs in MOFid space that enriches high-performing candidates, improves predicted separation performance, and maintains broad chemical and topological diversity.
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. Here, we present LEMO Agent, a large-language-model agent framework for closed-loop inverse design of gas-separation MOFs in MOFid space. LEMO Agent couples language-based candidate generation with MOFid standardization, explicit validity checking, Transformer-based property prediction, structured design memory, and multi-island exploration. Through iterative generate--validate--evaluate--remember cycles, the agent uses feedback from both successful and failed candidates to guide chemically constrained search across linker, metal, and topology choices. We evaluate LEMO Agent on CH$_4$/N$_2$ and CO$_2$/N$_2$ separation tasks. Compared with representative generative, optimization, and agentic baselines, LEMO Agent enriches high-performing candidates, improves predicted separation performance, and maintains broad chemical and topological diversity. Selected candidates are further reconstructed, evaluated by GCMC simulations, and passed through an experimental down-selection workflow based on chemical feasibility and ligand purchasability, leading to initial wet-lab synthesis and SEM characterization. These results demonstrate that large language model agents can serve as interpretable and scalable design engines for accelerating MOF discovery beyond conventional fixed-library screening.
This work introduces a domain specific language (DSL)-guided strategy to improve the reasoning and design capability of LLM agents by translating natural language design rules into symbolic predicates encoded in a predefined chemistry DSL, and developed a multi-agent materials design framework.
Dong Hyeon Mok, Seoin Back, Victor Fung et al.· 0 citations
Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are highly tunable in pore structure and chemical environment, yet their discovery remains slow and fragmented. Synthesis reports are often difficult to compare, characterization data are laborious to interpret, and computational predictions rarely guide experiments directly. Recent advances in large language models (LLM) have enabled the development of artificial intelligence (AI) agents that can interpret research goals, search the literature and databases, call external tools, and adapt workflows based on intermediate results. In this review, we distinguish three stages of AI-agent development in MOFs and COFs research: LLM-native, human-mediated systems; database-grounded, tool-using agents; and experiment-integrated, feedback-driven platforms. This progression reflects increasing scientific grounding and experimental agency. In our view, further progress will depend less on scaling language models alone than on developing traceable machine-actionable data, chemistry-aware validation, persistent experimental memory, and robust interfaces between AI agents and laboratory automation.
Jiayu Yu, Zihao Jiang, Donglin He· AI Agent· 0 citations
RF-Agent is presented, which addresses the gap in domain-specific RF reasoning through textbook-driven knowledge distillation through a multi-agent Question-Thinking-Solution-Answer pipeline and provides a reusable foundation for future work on LLM-aided RF circuit design.
Yueqi Xing, Houbo He, Jolie Wang et al.· 0 citations
By connecting the heterogeneous stages of computational materials discovery, the LLM-based agents of MAESTRO can operate across application domains and uncover high-performance materials that conventional screening approaches would be unlikely to consider.
Yuntong Chen, Ju Huang, Yu Liu et al.· 0 citations
CatDiT is presented, a unified framework for inverse catalyst design that generates valid and novel structures ranging from intermetallic alloys to oxide surfaces and establishes CatDiT as a practical and scalable approach for property-directed catalyst inverse design and targeted catalyst generation.
Hayoung Doo, Dong Hyeon Mok, S. Back et al.· 0 citations
Metal-organic frameworks (MOFs) and MOF-like porous materials exhibit vast structural diversity and support critical applications in gas storage, separations, and catalysis. Predictive modeling remains difficult because their structure-property relationships are multiscale and cage-like, governed by both local chemical environments and global pore-network topology. These challenges, together with sparse and unevenly distributed labeled data, hinder generalization across material families. We develop an interaction topology theory and propose the interaction topological transformer (ITT), a data-efficient framework that captures materials information across multiple scales and levels, including structural, elemental, atomic, and pairwise-elemental organization. ITT extracts scale-aware features reflecting both compositional and relational structures in complex porous frameworks and integrates them through a transformer architecture for joint reasoning across scales. Using self-supervised pretraining on more than 0.6 million unlabeled structures followed by supervised fine-tuning, ITT achieves accurate, transferable, state-of-the-art predictions for adsorption, transport, and stability properties across 17 tasks, providing a principled and scalable strategy for learning-guided discovery in diverse MOF-like materials.
