ZEBRA‐Prop: A Zero‐Shot Embedding‐Based Rapid and Accessible Regression Model for Materials Properties

Large language models (LLMs) exhibit substantial potential across diverse scientific disciplines, including materials science. A property prediction framework, ZEBRA‐Prop (Zero‐Shot Embedding‐Based Rapid and Accessible Regression Model for Materials Properties), is presented here as an extension of LLM‐Prop. In contrast to LLM‐Prop, which requires task‐specific fine‐tuning of the LLM, ZEBRA‐Prop keeps the text encoder frozen and eliminates task‐specific fine‐tuning of the LLM, thereby reducing computational cost and enabling rapid model training. The framework employs MatBERT, a language model pretrained on materials science literature, to enhance predictive capability. Multiple textual embeddings are incorporated through a learnable weighting mechanism, which alleviates the context‐length constraints inherent in LLM‐Prop and facilitates effective integration of diverse textual representations. Evaluation is conducted using two datasets: the TextEdge dataset (~140,000 entries) and an in‐house dataset (~2000 entries) derived from the Materials Project database, with physical properties obtained from first‐principles calculations. The predictive performance of ZEBRA‐Prop is close to that of LLM‐Prop, while the training time is reduced by approximately 95%. The performance improvements are attributable to three principal factors: domain‐specific LLM utilization, diversified textual descriptions, and systematic text preprocessing. ZEBRA‐Prop is a scalable, computationally efficient framework that supports accelerated materials discovery, particularly under limited computational resources.