Regression with Large Language Models for Materials and Molecular Property Prediction

ArXi:2409.06080v2 Announce Type: replace-cross We nstrate the ability of large language models (LLMs) to perform material and molecular property regression tasks, a significant deviation from the conventional LLM use case. We benchmark the Large Language Model Meta AI (LLaMA) 3 on several molecular properties in the QM9 dataset and 28 materials properties. Only composition-based input strings are used as the model input and we fine tune on only the generative loss.