Multi-fidelity transfer learning for quantum chemical data using a robust density functional tight binding baseline

Title data

Cui, Mengnan ; Reuter, Karsten ; Margraf, Johannes T.:
Multi-fidelity transfer learning for quantum chemical data using a robust density functional tight binding baseline.
In: Machine Learning: Science and Technology. Vol. 6 (2025) . - 015071.
ISSN 2632-2153
DOI: https://doi.org/10.1088/2632-2153/adc222

Official URL:

Abstract in another language

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.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Institutions of the University:	Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning > Chair Physical Chemistry V - Theory and Machine Learning - Univ.-Prof. Dr. Johannes Theo Margraf Research Institutions > Central research institutes > Bayerisches Zentrum für Batterietechnik - BayBatt
Result of work at the UBT:	Yes
DDC Subjects:	500 Science > 540 Chemistry
Date Deposited:	31 Mar 2025 05:54
Last Modified:	31 Mar 2025 05:54
URI:	https://eref.uni-bayreuth.de/id/eprint/93049