  Large language models (LLMs) bear promise as a fast and accurate material
modeling paradigm for evaluation, analysis, and design. Their vast number of
trainable parameters necessitates a wealth of data to achieve accuracy and
mitigate overfitting. However, experimental measurements are often limited and
costly to obtain in sufficient quantities for finetuning. To this end, we
present a physics-based training pipeline that tackles the pathology of data
scarcity. The core enabler is a physics-based modeling framework that generates
a multitude of synthetic data to align the LLM to a physically consistent
initial state before finetuning. Our framework features a two-phase training
strategy: (1) utilizing the large-in-amount while less accurate synthetic data
for supervised pretraining, and (2) finetuning the phase-1 model with limited
experimental data. We empirically demonstrate that supervised pretraining is
vital to obtaining accurate finetuned LLMs, via the lens of learning polymer
flammability metrics where cone calorimeter data is sparse.
