Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917549523861504 |
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| author | Staley, Edward W. Arbaugh, Tom Pekala, Michael New, Alexander Stiles, Christopher D. Le, Nam Q. Bassen, Gregory Bunstine, Wyatt McQueen, Tyrel |
| author_facet | Staley, Edward W. Arbaugh, Tom Pekala, Michael New, Alexander Stiles, Christopher D. Le, Nam Q. Bassen, Gregory Bunstine, Wyatt McQueen, Tyrel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00315 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials Staley, Edward W. Arbaugh, Tom Pekala, Michael New, Alexander Stiles, Christopher D. Le, Nam Q. Bassen, Gregory Bunstine, Wyatt McQueen, Tyrel Artificial Intelligence Materials Science 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. |
| title | Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials |
| topic | Artificial Intelligence Materials Science |
| url | https://arxiv.org/abs/2606.00315 |