Papers › Benchmarking Data Efficiency in Δ-ML and Multifidelity Models for Quantum Chemistry
Benchmarking Data Efficiency in Δ-ML and Multifidelity Models for Quantum Chemistry
Vivin Vinod, Peter Zaspel
The development of machine learning (ML) methods has made quantum chemistry (QC) calculations more accessible by reducing the compute cost incurred in conventional QC methods. This has since been translated into the overhead cost of generating training data. Increased work in reducing the cost of generating training data resulted in the development of Δ-ML and multifidelity machine learning methods which use data at more than one QC level of accuracy, or fidelity. This work compares the data costs associated with Δ-ML, multifidelity machine learning (MFML), and optimized MFML (o-MFML) in contrast with a newly introduced MultifidelityΔ-Machine Learning (MFΔML) method for the prediction of ground state energies, vertical excitation energies, and the magnitude of electronic contribution of molecular dipole moments from the multifidelity benchmark dataset QeMFi. This assessment is made on the basis of training data generation cost associated with each model and is compared with the single fidelity kernel ridge regression (KRR) case. The results indicate that the use of multifidelity methods surpasses the standard Δ-ML approaches in cases of a large number of predictions. For applications which require only a few evaluations to be made using ML models, while the Δ-ML method might be favored, the MFΔML method is shown to be more efficient.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections