Using GPT-4 in parameter selection of polymer informatics: improving predictive accuracy amidst data scarcity and 'Ugly Duckling' dilemma

Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current methodologies don't entirely bypass this theorem and may lead to decreased accuracy with unfamiliar data. We propose using OpenAI generative pretrained transformer 4 (GPT-4) language model for explanatory variable selection, leveraging its extensive knowledge and logical reasoning capabilities to embed domain knowledge in tasks predicting structure-property correlations, such as the refractive index of polymers. This can partially alleviate challenges posed by the "Ugly Duckling" theorem and limited data availability. Data scarcity in materials informatics hinders structure-property relationships. Using GPT-4 can address challenges, improving predictions like polymer refractive indices.