CHEMREASONER: Heuristic Search over a Large Language Model's Knowledge Space using Quantum-Chemical Feedback

arXiv:2402.10980v1 Announce Type: cross
Abstract: The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent actively searches for highly effective catalysts via the iterative combination of large language model (LLM)-derived hypotheses and atomistic graph neural …

abstract arxiv chemistry computational cs.ai cs.ce cs.lg design discovery feedback framework future knowledge language language model large language large language model physics.chem-ph processes quantum reasoning screening search space sustainable transition type