Due to the difficulty in identifying confident labels for hemilability, the positive and negative labels identified by semi-supervised learning in previous work are used to train models for identifying hemilabile ligands (SI Appendix, Table ). and). Hyperparameter tuning was performed with the training and validation data for 100 epochs using either 25 (coordinating atom models) or 50 (hemilability models) combinations of the number of message-passing steps, dropout, number of FFN layers, and hidden layer size using Chemprop's implementation of hyperopt (version 0.2.7) (SI Appendix, Table and Table ). All models were trained for 100 epochs using the cross-entropy loss function, rectified linear unit (ReLU) activation function, and identified optimal hyperparameters. The validation data was used for early stopping to prevent overfitting, while the test data was used for evaluating model performance. Additional ML-predicted QM descriptors are predicted using the qmdesc package (version 1.0.5) (SI Appendix, Table ). The source codes of Chemprop and qmdesc were modified slightly to increase their functionality in atom-level prediction tasks (SI Appendix, Text S2).