We trained kernel ridge regression (KRR) models using the Scikit-learn package v1.0.2. We used a radial basis function kernel and the GridSearchCV function to optimize the KRR hyperparameters (Supporting Information Table ). We trained random forest models with 5,000 trees and artificial neural networks (ANNs) and using the Keras v2.12.0 Python package with a Tensorflow v2.12.0 backend (Supporting Information Tables ). We used the Hyperopt v0.2.7 package for hyperparameter tuning with the ANN models. Each digitized isotherm contains between 20 and 40 datapoints, and we took each point in the isotherm as a separate data point for training purposes. This means that our training set has large subsets in which all of the structural descriptors and temperature are the same and only the pressure descriptor is changing.