A pairwise ranking model over a shared embedding space outperforms prior fixed-vocabulary classifiers for inorganic retrosynthesis, especially when ranking many candidate precursor sets.
Investigating the impact of pretraining for the encoder with the Top-K accuracy
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Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning
A pairwise ranking model over a shared embedding space outperforms prior fixed-vocabulary classifiers for inorganic retrosynthesis, especially when ranking many candidate precursor sets.