  Large language models are a form of artificial intelligence systems whose
primary knowledge consists of the statistical patterns, semantic relationships,
and syntactical structures of language1. Despite their limited forms of
"knowledge", these systems are adept at numerous complex tasks including
creative writing, storytelling, translation, question-answering, summarization,
and computer code generation. However, they have yet to demonstrate advanced
applications in natural science. Here we show how large language models can
perform scientific synthesis, inference, and explanation. We present a method
for using general-purpose large language models to make inferences from
scientific datasets of the form usually associated with special-purpose machine
learning algorithms. We show that the large language model can augment this
"knowledge" by synthesizing from the scientific literature. When a conventional
machine learning system is augmented with this synthesized and inferred
knowledge it can outperform the current state of the art across a range of
benchmark tasks for predicting molecular properties. This approach has the
further advantage that the large language model can explain the machine
learning system's predictions. We anticipate that our framework will open new
avenues for AI to accelerate the pace of scientific discovery.
