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Dong Hyeon Mok

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Preprint Jul 2026

Symbolic Predicate-Guided Language Agents for Inverse Design of Perovskite Oxides

Efficient discovery of high-performance materials has been pursued through a variety of data- and AI-driven strategies, among which inverse design, generating materials from desired target properties, has emerged as an important paradigm. Large language models (LLMs) offer a complementary route for inverse materials design because their reasoning and in-context learning capability can be used not only to propose candidates but also to demonstrate interpretable design principles. In this work, we introduce 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. These predicates allow the LLM agent to obtain statistical evidence from the accumulated materials data, enabling the agent to evaluate and refine its own reasoning during the design loop. Based on this strategy, we developed a multi-agent materials design framework, called Operational Rule-grounded CHEmical Search Through Reasoning Agents (ORCHESTRA), and applied it to the inverse design of double perovskite oxides under multiple target-property objectives. The results show that symbolic predicates help the LLM identify unsupported rules, validate newly proposed rules and improve the rule store over iterative design cycles. Compared with a strategy relying only on natural language rules, the DSL-guided framework showed the potential to improve materials design performance, particularly for challenging target properties. These findings suggest that mathematical and statistical grounding can enhance the reasoning capability of LLM agents in materials science and that LLM-based inverse design can be performed effectively without large task-specific datasets or additional model training.

Dong Hyeon Mok, Seoin Back, Victor Fung et al. · 0 citations
Preprint Jul 2026

Catalyst Diffusion Transformer: Generative Inverse Design of Heterogeneous Catalysts

The vast chemical design space and complex, interdependent design variables make catalyst discovery for targeted properties highly labor- and resource-intensive. Although generative models have emerged as a promising solution, existing approaches are generally limited to single-property conditioning or narrow chemical spaces. Here, we present Catalyst Diffusion Transformer (CatDiT), a unified framework for inverse catalyst design that generates valid and novel structures ranging from intermetallic alloys to oxide surfaces. By learning compressed latent representations, CatDiT enables efficient training and rapid sampling while supporting simultaneous conditioning on adsorbate type, binding energy, and catalyst class. The model provides reliable control of discrete properties and directional control of continuous properties, enriching candidate pools for reaction-specific catalyst discovery. As a representative application, multi-conditional generation for the nitrogen reduction reaction (NRR) yields 28 density functional theory (DFT)-relaxed alloy candidates that satisfy the target activity window and lie above the pure-metal *N-*H scaling line, corresponding to a ~1.5-fold enrichment over the source distribution. These results establish 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