2 Method and Computational Details ML-IAMs are characterized by two main features: model architecture and descriptor. For the majority of ML-IAMs, these features are structured such that parameters for interactions between atoms of various types are inseparable; introduction of an additional atom type requires generating a new model with all parameters updated. As a consequence, ML-IAMs are generally fit a-la-carte for a given target system, atomic composition, and set of conditions. Specifically, use of an atom-centered descriptor or use of complex architecture (e.g., neural network, graph based, etc) generally precludes generating models that are explicitly compositionally-extensible. In the section below, we will show that the clusterbased descriptor and parametrically linear form used by the ChIMES ML-IAM overcomes this limitation, enabling a unique opportunity for developing chemically extensible models through a hierarchical transfer learning strategy.