# Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties

arXiv: https://arxiv.org/abs/2407.17087 · HTML: https://mlsanity.com/paper/2407.17087/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Machine learning for electronically excited states of molecules](https://mlsanity.com/paper/2007.05320/) (2020)
- [Multitask methods for predicting molecular properties from heterogeneous data](https://mlsanity.com/paper/2401.17898/) (2024)
- [QeMFi: A Multifidelity Dataset of Quantum Chemical Properties of Diverse Molecules](https://mlsanity.com/paper/2406.14149/) (2024)
- [Multi-fidelity Gaussian process surrogate modeling for regression problems in physics](https://mlsanity.com/paper/2404.11965/) (2024)

## Built on by (most cited first, 4 of 4)

- [Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies](https://mlsanity.com/paper/2410.11392/) (2024)
- [LFaB: Low fidelity as Bias for Active Learning in the chemical configuration space](https://mlsanity.com/paper/2508.15577/) (2025)
- [Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning](https://mlsanity.com/paper/2606.02662/) (2026)
- [Benchmarking Data Efficiency in Delta-ML and Multifidelity Models for Quantum Chemistry](https://mlsanity.com/paper/2410.11391/) (2024)

## Abstract (arXiv)

Multifidelity machine learning (MFML) for quantum chemical (QC) properties has seen strong development in the recent years. The method has been shown to reduce the cost of generating training data for high-accuracy low-cost ML models. In such a set-up, the ML models are trained on molecular geometries and some property of interest computed at various computational chemistry accuracies, or fidelities. These are then combined in training the MFML models. In some multifidelity models, the training data is required to be nested, that is the same molecular geometries are included to calculate the property across all the fidelities. In these multifidelity models, the requirement of a nested configuration restricts the kind of sampling that can be performed while selection training samples at different fidelities. This work assesses the use of non-nested training data for two of these multifidelity methods, namely MFML and optimized MFML (o-MFML). The assessment is carried out for the prediction of ground state energies and first vertical excitation energies of a diverse collection of molecules of the CheMFi dataset. Results indicate that the MFML method still requires a nested structure of training data across the fidelities. However, the o-MFML method shows promising results for non-nested multifidelity training data with model errors comparable to the nested configurations.