Development of Machine Learning Atomistic Potential for Molecular Simulation of Hematite–Water Interfaces
A novel approach for constructing a machine-learned potential energy surface (MLP) from unlabeled training data is presented. Utilizing neural networks augmented with a pool-based active learning sampling method, a potential energy surface (PES) is developed for the accurate modeling of interfaces o...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
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| Series: | Crystals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4352/14/11/930 |
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