Multi-fidelity transfer learning for quantum chemical data using a robust density functional tight binding baseline
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 curr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adc222 |
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