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arXiv 2005.07046cond-mat.mtrl-sciphysics.comp-ph
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高斯近似势模型中声子精度与高传递性的结合

Combining phonon accuracy with high transferability in Gaussian approximation potential models

Janine George, Geoffroy Hautier, Albert P. Bartók, Gábor Csányi, Volker L. Deringer

摘要:

机器学习驱动的原子间势,包括高斯近似势(GAP)模型,是原子模拟的新兴工具。在这里,我们解决的方法问题,如何可以适合GAP模型,准确地预测振动特性的特定区域的配置空间,同时保持灵活性和可转移性。我们使用的GAP拟合的自适应正则化,其与任何给定原子上的绝对力大小成比例,从而探索GAP正则化作为“预期误差”的贝叶斯解释,以及其对感兴趣的材料的物理性质预测的影响。该方法能够很好地预测结构不同的硅同素异形体的声子模式(在0.1-0.2 THz范围内),并且它可以与现有的拟合数据库相结合,以实现高可转移性。这些发现和工作流程预计将有助于更普遍的间隙驱动材料建模。

英文摘要:

Machine learning driven interatomic potentials, including Gaussian approximation potential (GAP) models, are emerging tools for atomistic simulations. Here, we address the methodological question of how one can fit GAP models that accurately predict vibrational properties in specific regions of configuration space, whilst retaining flexibility and transferability to others. We use an adaptive regularization of the GAP fit that scales with the absolute force magnitude on any given atom, thereby exploring the Bayesian interpretation of GAP regularization as an "expected error", and its impact on the prediction of physical properties for a material of interest. The approach enables excellent predictions of phonon modes (to within 0.1-0.2 THz) for structurally diverse silicon allotropes, and it can be coupled with existing fitting databases for high transferability. These findings and workflows are expected to be useful for GAP-driven materials modeling more generally.

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