Progress in both Machine Learning (ML) and conventional Quantum Chemistry
(QC) computational methods have resulted in high accuracy ML models for QC
properties ranging from atomization energies to excitation energies. Various
datasets such as MD17, MD22, and WS22, which consist of properties calculated
at some level of QC method, or fidelity, have been generated to benchmark such
ML models. The term fidelity refers to the accuracy of the chosen QC method to
the actual real value of the property. The higher the fidelity, the more
accurate the calculated property, albeit at a higher computational cost.
Research in multifidelity ML (MFML) methods, where ML models are trained on
data from more than one numerical QC method, has shown the effectiveness of
such models over single fidelity methods. Much research is progressing in this
direction for diverse applications ranging from energy band gaps to excitation
energies. A major hurdle for effective research in this field of research in
the community is the lack of a diverse multifidelity dataset for benchmarking.
Here, we present a comprehensive multifidelity dataset drawn from the WS22
molecular conformations. We provide the quantum Chemistry MultiFidelity
(CheMFi) dataset consisting of five fidelities calculated with the TD-DFT
formalism. The fidelities differ in their basis set choice and are namely:
STO-3G, 3-21G, 6-31G, def2-SVP, and def2-TZVP. CheMFi offers to the community a
variety of QC properties including vertical excitation energies, oscillator
strengths, molecular dipole moments, and ground state energies. In addition to
the dataset, multifidelity benchmarks are set with state-of-the-art MFML and
optimized-MFML

提供了一个综合的多保真度数据集 CheMFi，其中包括机器学习、量子化学、多保真度机器学习方法、基于 TD-DFT 的不同保真度数据集（STO-3G，3-21G，6-31G，def2-SVP 和 def2-TZVP）以及用于基准测试的多保真度基准模型。