Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910223805972480 |
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| author | Hellyar, Tom Salzbrenner, Pascal T. Cooke, Peter I. C. Pickard, Chris J. Habershon, Scott Pártay, Livia B. |
| author_facet | Hellyar, Tom Salzbrenner, Pascal T. Cooke, Peter I. C. Pickard, Chris J. Habershon, Scott Pártay, Livia B. |
| contents | We compare the predicted phase behaviour of lead (Pb) using three different interatomic potential models, including an embedded atom method (EAM), a modified embedded atom method (MEAM), and a neural network-based machine-learned model in the form of an ephemeral data-derived potential (EDDP). Using nested sampling and replica-exchange nested sampling simulations, we computed thermodynamic and structural properties at pressures up to 60 GPa, mapping both melting behaviour and solid-phase stability. Both the EAM and MEAM models predict the face-centred cubic (FCC) phase to remain stable up to approximately 60 GPa. In contrast, the EDDP model captures the experimentally-observed FCC-to-hexagonal close-packed (HCP) transition at around 15 GPa. These results highlight the importance of training data and model flexibility in accurately describing high-pressure phase behaviour, and demonstrate the effectiveness of nested sampling as a robust framework for exploring phase stability in materials. Particularly, the combination of nested sampling with modern machine-learned interatomic potentials - delivering near ab initio accuracy at tractable cost - opens the door to truly predictive and exhaustive exploration. EDDPs trained on diverse, out-of-equilibrium configurations appear particularly well suited to this task, offering a robust and transferable framework for unbiased phase discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16018 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead Hellyar, Tom Salzbrenner, Pascal T. Cooke, Peter I. C. Pickard, Chris J. Habershon, Scott Pártay, Livia B. Materials Science Statistical Mechanics We compare the predicted phase behaviour of lead (Pb) using three different interatomic potential models, including an embedded atom method (EAM), a modified embedded atom method (MEAM), and a neural network-based machine-learned model in the form of an ephemeral data-derived potential (EDDP). Using nested sampling and replica-exchange nested sampling simulations, we computed thermodynamic and structural properties at pressures up to 60 GPa, mapping both melting behaviour and solid-phase stability. Both the EAM and MEAM models predict the face-centred cubic (FCC) phase to remain stable up to approximately 60 GPa. In contrast, the EDDP model captures the experimentally-observed FCC-to-hexagonal close-packed (HCP) transition at around 15 GPa. These results highlight the importance of training data and model flexibility in accurately describing high-pressure phase behaviour, and demonstrate the effectiveness of nested sampling as a robust framework for exploring phase stability in materials. Particularly, the combination of nested sampling with modern machine-learned interatomic potentials - delivering near ab initio accuracy at tractable cost - opens the door to truly predictive and exhaustive exploration. EDDPs trained on diverse, out-of-equilibrium configurations appear particularly well suited to this task, offering a robust and transferable framework for unbiased phase discovery. |
| title | Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead |
| topic | Materials Science Statistical Mechanics |
| url | https://arxiv.org/abs/2605.16018 |