Experimental Establishment of Phase Diagrams Guided by Uncertainty Sampling: An Application to the Deposition of Zn–Sn–P Films by Molecular Beam Epitaxy,ACS Materials Letters

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Experimental Establishment of Phase Diagrams Guided by Uncertainty Sampling: An Application to the Deposition of Zn–Sn–P Films by Molecular Beam Epitaxy
ACS Materials Letters ( IF 9.6 ) Pub Date : 2020-04-24 , DOI: 10.1021/acsmaterialslett.0c00104
Ryoji Katsube ₁ , Kei Terayama _{2,

3,

4,

5} , Ryo Tamura _{2,

6,

7,

8} , Yoshitaro Nose ₁

Affiliation

The construction of phase diagrams is an essential step in the development of materials and devices, but this process requires considerable time and the efforts of well-established researchers. Recently, a machine learning method based on uncertainty sampling, phase diagram construction (PDC), was developed in order to minimize the number of required experiments. In the present study, we, for the first time, demonstrate PDC-assisted experimental determination of an uninvestigated phase diagram for the deposition of Zn–Sn–P films by molecular beam epitaxy (MBE). Interestingly, without any background knowledge of materials science, an undetected phase in the initial dataset was detected after only 4 cycles of PDC-assisted experiments, and the phase diagram was sufficiently refined after only 11 cycles. Based on the results and thermodynamic discussions, we confirm the effectiveness of PDC on the construction of phase diagrams for MBE process. The demonstration in this study shows the machine-learning method, PDC, will accelerate discovery and development of materials; furthermore, we discuss how to use PDC more effectively, considering the objectives involved in the construction of phase diagrams.

中文翻译：

不确定性采样指导相图的实验建立：在分子束外延沉积Zn-Sn-P薄膜中的应用

相图的构建是材料和设备开发中必不可少的步骤，但是此过程需要大量的时间和成熟的研究人员的努力。最近，为了减少所需实验的数量，开发了一种基于不确定性采样，相图构造（PDC）的机器学习方法。在本研究中，我们首次展示了PDC辅助实验确定的未经研究的相图，该相图用于通过分子束外延（MBE）沉积Zn-Sn-P膜。有趣的是，在没有任何材料科学背景知识的情况下，仅在PDC辅助实验的4个循环后就检测到初始数据集中未检测到的相，而在11个循环后就充分细化了相图。基于结果和热力学讨论，我们证实了PDC在MBE工艺相图构建中的有效性。本研究中的演示表明，机器学习方法PDC将加速材料的发现和开发。此外，考虑到构建相图的目标，我们讨论了如何更有效地使用PDC。

更新日期：2020-04-24

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