Fig. 1: Overview illustration of the datasets and tasks addressed in this work.
From: Leveraging large language models for predictive chemistry
In this work, we benchmark GPT-3 on datasets spanning the chemical space from molecules over materials to reactions (Supplementary Note 1). On these datasets, we investigate different tasks ranging from classification, that is, predicting a class (for example, ‘high’, ‘low’) given a text representation of a molecule, material or reaction, to regression, that is, prediction of floating point numbers, to inverse design—the prediction of molecules. Metal–organic framework rendering created with iRASPA60.
