Surrogate‐Guided Latent‐Space Reinforcement Learning for Generative Ligand Design in Am(III)/Eu(III) Separation

Am(III)/Eu(III) separation is a stringent benchmark for actinide/lanthanide partitioning because the two trivalent ions exhibit closely related size and solution chemistry, making selectivity highly dependent on subtle ligand‐controlled coordination differences. Herein, we report a surrogate‐guided latent‐space reinforcement learning workflow for generative ligand design under sparse and heterogeneous extraction‐data conditions. A junction‐tree variational autoencoder was pretrained on ligand structures extracted from CSD‐derived coordination compounds and fine‐tuned on literature‐reported Am/Eu extractants to construct a chemically valid ligand latent space. A condition‐aware XGBoost surrogate model predicted ln(SF) from ligand, solvent, and experimental‐condition features and supplied the reward signal for proximal policy optimization (PPO). The surrogate retained useful ranking ability, including under a strict ligand‐level split, while PPO enriched multiple regions associated with high surrogate‐predicted performance. The generated candidates recapitulated established soft‐donor and N‐heterocyclic design patterns while suggesting new structural variants in the learned ligand space. This workflow demonstrates how surrogate‐guided generative optimization can translate limited Am/Eu extraction data into chemically meaningful ligand‐design hypotheses.