Molecules and Materials in Conversation: Encoding and Decoding Chemistry with Language Models

Using text data in traditional machine-learning approaches is also difficult. Recent developments in applying large language models (LLMs) to chemistry have shown promise in overcoming this challenge. LLMs can convert unstructured text data into structured form and can even directly solve predictive tasks in chemistry. In my talk, I will present the impressive results of using LLMs, showcasing how they can autonomously utilize tools and leverage structured data and “fuzzy” inductive biases. To enable the training of a chemical-specific large language model, we have curated a new dataset along with a comprehensive toolset to utilize datasets from knowledge graphs, preprints, and unlabeled molecules. To evaluate frontier models trained on such a dataset, we specifically designed a benchmark to evaluate the chemical knowledge and reasoning abilities. I will present the latest results, demonstrating the potential of LLMs in advancing chemical research.