从物理驱动的机器学习方法中氧化锌的非经典成核,The Journal of Physical Chemistry C

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从物理驱动的机器学习方法中氧化锌的非经典成核
The Journal of Physical Chemistry C ( IF 3.3 ) Pub Date : 2022-09-28 , DOI: 10.1021/acs.jpcc.2c06341
Jacek Goniakowski ₁ , Sarath Menon ₂ , Gaétan Laurens ₃ , Julien Lam ₄

Affiliation

在具有技术兴趣的复杂材料的模拟中，观察非经典成核途径仍然具有挑战性。这是因为它需要非常精确的力场来捕捉它们潜在的原子间相互作用的整体复杂性，以及能够区分竞争结晶相的先进结构分析。在这里，我们首先报告了使用物理 LassoLars 相互作用势方法构建并特别彻底验证了用于氧化锌相互作用的机器学习力场，这使我们甚至可以预测高温动态系统，例如 ZnO 熔体。然后，我们进行了几种类型的结晶模拟，并以原子精度跟踪了 ZnO 晶体的形成。我们的结果，使用基于键序参数的数据驱动方法对其进行了分析，证明了预成核簇和两步成核方案的存在，从而检索了在更简单的模型上做出的非经典成核途径的开创性预测。在新开发的自动非平衡热力学积分方法中对高温 ZnO 自由能的专门计算揭示了预测的非经典成核方案存在热力学偏差。

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Nonclassical Nucleation of Zinc Oxide from a Physically Motivated Machine-Learning Approach

Observing nonclassical nucleation pathways remains challenging in simulations of complex materials with technological interests. This is because it requires very accurate force fields that can capture the whole complexity of their underlying interatomic interactions and an advanced structural analysis able to discriminate between competing crystalline phases. Here, we first report the construction and particularly thorough validation of a machine-learning force field for zinc oxide interactions using the Physical LassoLars Interaction Potentials approach which allows us to be predictive even for high-temperature dynamical systems such as ZnO melt. Then, we carried out several types of crystallization simulations and followed the formation of ZnO crystals with atomistic precision. Our results, which were analyzed using a data-driven approach based on bond order parameters, demonstrate the presence of both prenucleation clusters and two-step nucleation scenarios, thus retrieving seminal predictions of nonclassical nucleation pathways made on much simpler models. Dedicated calculations of high-temperature ZnO free energy within a newly developed automated nonequilibrium thermodynamic integration method revealed the existence of a thermodynamic bias for the predicted nonclassical nucleation scenarios.

更新日期：2022-09-28

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