The quality of a machine-learned model of a chemical property is highly dependent on the size, breadth, and quality of the available data. Existing machine learning models for solvation energy have been trained using small databases of experimental data, which limits their applicability. One such database, FreeSolv, has been used extensively to create ma-chine learning (ML) models for hydrogenation energy based on diverse techniques including convolutional neural networks and fingerprint-based machine learning. Similar experimental databases, like those from the University of Minnesota, provide solvation data for many solvents and have also been used as the basis for machine learning models, as illustrated by pioneering work from Borhani et al and Subramanian et al. The limited size and chemical diversity of experimental data (only a few hundred molecules) makes them impractical to use with high-accuracy, deep learning models (e.g., Refs. ). This issue can be addressed by developing large and diverse datasets of solvation properties (¿100,000 molecules) via computations that provide sufficient representation of different regions of the chemical space.