Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials
Quick Take
A novel hybrid framework combines Large Language Models with thermodynamic databases for inorganic material synthesis planning, demonstrating that LLMs can generate more viable synthesis routes than classical algorithms in the niobium-oxygen system case study.
Key Points
- Hybrid framework evaluates LLMs for inorganic synthesis planning.
- Focus on niobium-oxygen system with multiple oxide phases.
- LLM-generated routes outperform classical path-planning algorithms.
- Classical methods serve as a foil, not direct competitors.
- Highlights the complexity of synthesis planning in materials science.
Article Excerpt
From source RSS / original summaryarXiv:2606. 00315v1 Announce Type: new 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.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →MindGames Arena Generalization Track: In2AI Solution with Delayed Per-Step Reward Attribution
The In2AI solution introduces delayed per-step reward attribution for training language model agents in multi-agent environments, achieving top performance on the MindGames Arena benchmark at NeurIPS 2025. An 8-billion-parameter model outperformed larger proprietary systems, including GPT-5, in competitive play, demonstrating enhanced stability and sample efficiency in reinforcement learning.