Supplementary Information for "Predicting Spin-Crossover Behavior in Metal-Organic Frameworks from Limited and Noisy Data Using Quantile Active Learning"

This record consists of the results from the work for "Predicting Spin-Crossover Behavior in Metal-Organic Frameworks from Limited and Noisy Data Using Quantile Active Learning" (https://arxiv.org/abs/2603.03545). It includes the AiiDA-Quantum ESPRESSO workflows, datasets, and DFT-computed and predicted (ΔEH–L) values, and the trained models developed and used in this work.