Machine learning the screening factor in the soft bond valence approach for rapid crystal structure estimation†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Keisuke Kameda, Takaaki Ariga, Kazuma Ito, Manabu Ihara and Sergei Manzhos
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Abstract

The development of novel functional ceramics is critically important for several applications, including the design of better electrochemical batteries and fuel cells, in particular solid oxide fuel cells. Computational prescreening and selection of such materials can help discover novel materials but is also challenging due to the high cost of electronic structure calculations which would be needed to compute the structures and properties of interest such as the material's stability and ion diffusion properties. The soft bond valence (SoftBV) approach is attractive for rapid prescreening among multiple compositions and structures, but the simplicity of the approximation can make the results inaccurate. In this study, we explore the possibility of enhancing the accuracy of the SoftBV approach when estimating crystal structures by adapting the parameters of the approximation to the chemical composition. Specifically, on the examples of perovskite- and spinel-type oxides that have been proposed as promising solid-state ionic conductors, the screening factor – an independent parameter of the SoftBV approximation – is modeled using linear and non-linear methods as a function of descriptors of the chemical composition. We find that making the screening factor a function of composition can noticeably improve the ability of the SoftBV approximation to correctly model structures, in particular new, putative crystal structures whose structural parameters are yet unknown. We also analyze the relative importance of nonlinearity and coupling in improving the model and find that while the quality of the model is improved by including nonlinearity, coupling is relatively unimportant. While using a neural network showed practically no improvement over linear regression, the recently proposed GPR-NN method that is a hybrid between a single hidden layer neural network and kernel regression showed substantial improvement, enabling the prediction of structural parameters of new ceramics with accuracy on the order of 1%.

Abstract Image

Abstract Image

机器学习软键价方法中的筛选因子,实现快速晶体结构估算
新型功能陶瓷的开发对多种应用至关重要,包括设计更好的电化学电池和燃料电池,特别是固体氧化物燃料电池。对此类材料进行计算预筛选和选择有助于发现新型材料,但由于计算材料的稳定性和离子扩散特性等相关结构和特性需要进行高成本的电子结构计算,因此也具有挑战性。软键价(SoftBV)方法对于在多种成分和结构中进行快速预筛选很有吸引力,但近似的简单性可能会使结果不准确。在本研究中,我们探讨了在估计晶体结构时,通过根据化学成分调整近似值的参数来提高 SoftBV 方法准确性的可能性。具体来说,我们以被提出有望成为固态离子导体的包晶型和尖晶石型氧化物为例,使用线性和非线性方法将筛选因子(SoftBV 近似的独立参数)作为化学成分描述符的函数进行建模。我们发现,将屏蔽因子作为化学成分的函数可以明显改善 SoftBV 近似方法正确建立结构模型的能力,特别是那些结构参数尚不清楚的新的假定晶体结构。我们还分析了非线性和耦合在改进模型方面的相对重要性,发现虽然加入非线性可以提高模型质量,但耦合相对来说并不重要。与线性回归相比,使用神经网络几乎没有任何改进,而最近提出的 GPR-NN 方法(单隐层神经网络与核回归的混合方法)则有了大幅改进,使新型陶瓷结构参数的预测精度达到了 1%。
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CiteScore
2.80
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