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

TL;DR

This paper introduces QeMFi, a comprehensive multifidelity quantum chemistry dataset with diverse properties and computational fidelities, enabling benchmarking and development of multifidelity machine learning models in quantum chemistry.

Contribution

The paper presents QeMFi, the first diverse multifidelity quantum chemistry dataset with multiple properties and basis set fidelities for benchmarking ML models.

Findings

Provides a variety of quantum chemical properties including excitation energies and dipole moments.

Includes computational times for fidelity comparison.

Facilitates benchmarking of multifidelity ML models in quantum chemistry.

Abstract

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 some 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. Much research is progressing in this direction for diverse applications ranging from energy band gaps to excitation energies. One hurdle for effective research here is the lack of a diverse multifidelity dataset for benchmarking. We provide the Quantum chemistry MultiFidelity (QeMFi) dataset consisting of five fidelities calculated with the TD-DFT formalism. The fidelities differ in their basis set choice: STO-3G, 3-21G, 6-31G, def2-SVP, and def2-TZVP. QeMFi offers to the community a variety of QC properties such as vertical excitation properties and molecular dipole moments, further including QC computation times allowing for a time benefit benchmark of multifidelity models for ML-QC.