Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials

• URL: http://arxiv.org/abs/2606.00315v1
• Date: Fri, 29 May 2026 19:41:29 GMT
• Title: Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials
• Abstract summary: We introduce a novel framework to evaluate Large Language Models (LLMs) in inorganic synthesis planning.<n>We focus on the niobium-oxygen system, which features multiple industrially relevant oxide phases with well-characterized data.<n>In our evaluation setting, classical search methods serve primarily as a foil rather than a direct competitor.
• Score: 31.895328442910937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern generative machine learning (ML) models can propose novel inorganic crystalline materials with targeted properties; however, synthesis planning of these materials remains difficult due to the complexity of the associated physical processes and limited availability of computational tools. We introduce a novel hybrid framework to evaluate Large Language Models (LLMs) in inorganic synthesis planning by combining thermodynamic databases with simplified kinetics models to approximate realistic synthesis conditions. As a case study, we focus on the niobium-oxygen system, which features multiple industrially relevant oxide phases with well-characterized data. In computational simulations, we compare LLM-generated synthesis routes with classical path-planning algorithms, showing that the implicit priors in LLMs can yield more viable strategies. In our evaluation setting, classical search methods serve primarily as a foil rather than a direct competitor. This illustrates the relative complexity of the problem and highlights where the LLM's implicit priors add value.

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