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

Computer Science > Artificial Intelligence [Submitted on 29 May 2026] Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials Edward W. Staley, Tom Arbaugh, Michael Pekala, Alexander New, Christopher D. Stiles, Nam Q. Le, Gregory Bassen, Wyatt Bunstine, Tyrel McQueen 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. Comments: Accepted to the AI for Accelerated Materials Design (AI4Mat) Workshop at Neurips 2025 Subjects: Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci) Cite as: arXiv:2606.00315 [cs.AI]   (or arXiv:2606.00315v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.00315 Focus to learn more Submission history From: Edward Staley [view email] [v1] Fri, 29 May 2026 19:41:29 UTC (910 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cond-mat cond-mat.mtrl-sci cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)