Vinod, V., & Zaspel, P. (2024). Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties. arXiv preprint 2407.17087, http://arxiv.org/abs/2407.17087
Multi-fidelity methods in machine learning (ML) of quantum chemistry (QC) properties have made high accuracy low cost models more accessible to the community. These have been used in application for a range of properties including excitation energies. Most multi-fidelity methods usually require a nested configuration of the training data, that is, calculations for a geometry are to be made at the lower fidelities as well as the higher fidelities.
In a recent work, available as a preprint the authors, Vivin Vinod and Peter Zaspel assess a non-nested configuration of multi-fidelity machine learning (MFML) and optimized MFML (o-MFML) methods. Preliminary results suggest that while MFML would still require a nested data structure, o-MFML can generalize reasonably well over a non-nested training data structure. That is, o-MFML opens up avenues for the use of heterogeneous datasets reducing the requirement to make costly calculations for high-fidelity data.