| Abstract Scope |
Language model intelligence is revolutionizing the way we program materials simulations. However, the diversity of simulation scenarios renders it challenging to precisely transform human language into a tailored simulator. Here, using three functionalized types of language model, we propose a language-to-simulation (Lang2Sim) framework that enables interactive navigation on languaging a simulation engine. Unlike line-by-line coding of a target simulator, the language models interpret each simulator as an assembly of invariant tool function and its variant input-output pair, enabling precise Lang2Sim by rationalizing the tool categorization, customizing its input-output combinations, and distilling the simulator input into executable format. Importantly, each type of language model features a distinct processing of chat history to best balance its memory limit and information completeness, thus leveraging the model intelligence to unstructured nature of human request. Overall, this work establishes language model as an intelligent platform to unlock the era of languaging a simulation engine. |