Retro-Forge: A Multi-Step Pairwise Retrosynthesis Framework for Solid-State Materials Synthesis

AI-driven materials discovery has made remarkable strides in generating stable, unique, and novel structures at unprecedented scale, yet a critical gap remains between generation and realization: without valid synthesis routes, proposed materials stay in silico. Existing approaches to precursor prediction (PP)—the first and most consequential step of materials synthesis planning (MSP)—formulate it as a single-step problem, overlooking the established domain knowledge that solid-state reactions proceed pairwise. We introduce Retro-Forge, a multi-step pairwise retrosynthesis framework that for the first time casts PP as a sequence of learnable pairwise reactions. A single-step pairwise reactant prediction model is trained on a curated pairwise reaction dataset, expanded through synthetic data augmentation to address data scarcity. This model is composed recursively within a tree search to produce complete synthesis routes, and further optimized for this task via multi-step tree-RL. We construct a clean benchmark of 857 novel targets verified absent from all training data, on which Retro-Forge achieves state-of-the-art performance. Beyond precursor prediction accuracy, we independently corroborate that the large majority of its predicted pairwise steps are stoichiometrically balanced and thermodynamically favorable under DFT-derived reaction energies—demonstrating that the multi-step pairwise formulation is not only learnable but also chemically valid.