# Multi-step chemical mechanism prediction

Multi-step reaction mechanism prediction has largely stayed at the level of single elementary steps and generalizes poorly to unseen reaction spaces. GRMP couples a chemical mechanism knowledge graph, completed with expert templates, to a language model: a graph convolutional network generates candidate pathways that become preference data for direct preference optimization, and at inference a relation retrieval module pulls graph evidence to refine the pathway. GRMP reaches pathway accuracy of 48.1% on ChemMechQA and 57.9% on oMe-Gold, the highest among the compared methods.

Multi-step reaction mechanism prediction has largely stayed at the level of single elementary steps and generalizes poorly to unseen reaction spaces. GRMP couples a chemical mechanism knowledge graph, completed with expert templates, to a language model: a graph convolutional network generates candidate pathways that become preference data for direct preference optimization, and at inference a relation retrieval module pulls graph evidence to refine the pathway. GRMP reaches pathway accuracy of 48.1% on ChemMechQA and 57.9% on oMe-Gold, the highest among the compared methods.