Approximating quantum mechanical properties using machine learning (ML) is of significant interest for applications in catalysis, material and drug design . However, traditional physics-based methods are severely limited by computational requirements that scale poorly with system size . In the pursuit of accurate, scalable, and generalisable ML models, several different strategies have been proposed. One way to support this vision is by taking a purely data-driven approach and developing quantum machine learning (QML) models based on accurate physical methods such as density functional theory (DFT) combined with large and diverse collections of data (e.g. QM9 , QMugs , nablaDFT and QM7-X ). Another approach is to devise transfer learning datasets and algorithms that can extract useful patterns from less accurate, but cheaper and more scalable simulations that ultimately benefit predictions at a higher fidelity level ). At the same time, advances in GNN architectures and the ability to exploit specific features of quantum data such as atom positions and directional information such as bond angles and rotations are an active area of research that have produced state-of-the-art models ( ).