Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel inorganic materials, yet traditional methods in inorganic chemistry continue to rely on trial-and-error experimentation. Emerging machine-learning approaches struggle to generalize to entirely new reactions due to their reliance on known precursors, as they frame retrosynthesis as a multi-label classification task. To address these limitations, we propose Retro-Rank-In, a novel framework that reformulates the retrosynthesis problem by embedding target and precursor materials into a shared latent space and learning a pairwise ranker on a bipartite graph of inorganic compounds. We evaluate Retro-Rank-In's generalizability on challenging retrosynthesis dataset splits designed to mitigate data duplicates and overlaps. For instance, for Cr2AlB2, it correctly predicts the verified precursor pair CrB + Al despite never seeing them in training, a capability absent in prior work. Extensive experiments show that Retro-Rank-In sets a new state-of-the-art, particularly in out-of-distribution generalization and candidate set ranking, offering a powerful tool for accelerating inorganic material synthesis.
View on arXiv@article{prein2025_2502.04289,
title={ Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning },
author={ Thorben Prein and Elton Pan and Sami Haddouti and Marco Lorenz and Janik Jehkul and Tymoteusz Wilk and Cansu Moran and Menelaos Panagiotis Fotiadis and Artur P. Toshev and Elsa Olivetti and Jennifer L.M. Rupp },
journal={arXiv preprint arXiv:2502.04289},
year={ 2025 }
}