Multi-fidelity transfer learning for quantum chemical data using a robust density functional tight binding baseline
Year: 2025
Type: article
Abstract: Machine learning has revolutionized the development of interatomic potentials over the past decade, offering unparalleled computational speed without compromising accuracy. However, the performance of these models is highly dependent on the quality and amount of training data. Consequently, the current scarcity of high-fidelity datasets (i.e. beyond semilocal density functional theory) represents a significant challenge for further improvement. To address this, this study investigates the performance of transfer learning (TL) across multiple fidelities for both molecules and materials. Crucially, we disentangle the effects of multiple fidelities and different configuration/chemical spaces for pre-training and fine-tuning, in order to gain a deeper understanding of TL for chemical applications. This reveals that negative transfer, driven by noise from low-fidelity methods such as a density functional tight binding baseline, can significantly impact fine-tuned models. Despite this, the multi-fidelity approach demonstrates superior performance compared to single-fidelity learning. Interestingly, it even outperforms TL based on foundation models in some cases, by leveraging an optimal overlap of pre-training and fine-tuning chemical spaces.
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W4408556840