Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies.

Recent progress in machine learning (ML) has made high-accuracy quantum chemistry (QC) calculations more accessible. Of particular interest are multifidelity machine learning (MFML) methods, where training data from differing accuracies or fidelities are used. These methods usually employ a fixed scaling factor, γ, to relate the number of training samples across different fidelities, which reflects the cost and assumed sparsity of the data.

Predicting Molecular Energies of Small Organic Molecules With Multi-Fidelity Methods.

Multi-fidelity methods in machine learning (ML) have seen increasing usage for the prediction of quantum chemical properties. These methods, such as -ML and Multifidelity Machine Learning (MFML), have been shown to significantly reduce the computational cost of generating training data. This work implements and analyzes several multi-fidelity methods including -ML and MFML for the prediction of electronic molecular energies at DLPNO-CCSD(T) level, that is, at the level of coupled cluster theory including single and double excitations and perturbative triples corrections.

QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules.

Progress in both Machine Learning (ML) and Quantum Chemistry (QC) methods have resulted in high accuracy ML models for QC properties. Datasets such as MD17 and WS22 have been used to benchmark these models at a given level of QC method, or fidelity, which refers to the accuracy of the chosen QC method. Multifidelity ML (MFML) methods, where models are trained on data from more than one fidelity, have shown to be effective over single fidelity methods.

Multifidelity Machine Learning for Molecular Excitation Energies.

The accurate but fast calculation of molecular excited states is still a very challenging topic. For many applications, detailed knowledge of the energy funnel in larger molecular aggregates is of key importance, requiring highly accurate excitation energies. To this end, machine learning techniques can be a very useful tool, though the cost of generating highly accurate training data sets still remains a severe challenge.

Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited.

Inspired by Pople diagrams popular in quantum chemistry, we introduce a hierarchical scheme, based on the multilevel combination (C) technique, to combine various levels of approximations made when molecular energies are calculated. When combined with quantum machine learning (QML) models, the resulting CQML model is a generalized unified recursive kernel ridge regression that exploits correlations implicitly encoded in training data composed of multiple levels in multiple dimensions. Here, we have investigated up to three dimensions: chemical space, basis set, and electron correlation treatment.