Zhang Feng, Tamura Ryo, Zeng Fanyu, Kozawa Daichi, Kitaura Ryo
Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan.
Department of Chemistry, Nagoya University, Nagoya 464-8601, Japan.
ACS Appl Mater Interfaces. 2024 Oct 30;16(43):59109-59115. doi: 10.1021/acsami.4c15275. Epub 2024 Oct 15.
We applied Bayesian optimization (BO), a machine learning (ML) technique, to optimize the growth conditions of monolayer WS using photoluminescence (PL) intensity as the objective function. Through iterative experiments guided by BO, an improvement of 86.6% in PL intensity is achieved within 13 optimization rounds. Statistical analysis revealed the relationships between growth conditions and PL intensity, highlighting the importance of critical conditions, including the tungsten source concentration and Ar flow rate. Furthermore, the effectiveness of BO is demonstrated by comparison with random search, showing its ability to converge to optimal conditions with fewer iterations. This research highlights the potential of ML-driven approaches in accelerating material synthesis and optimization processes, paving the way for advances in two-dimensional (2D) material-based technologies.
我们应用了贝叶斯优化（BO）这一机器学习（ML）技术，以光致发光（PL）强度作为目标函数来优化单层WS的生长条件。在BO引导的迭代实验中，经过13轮优化，PL强度提高了86.6%。统计分析揭示了生长条件与PL强度之间的关系，突出了关键条件的重要性，包括钨源浓度和氩气流速。此外，通过与随机搜索进行比较，证明了BO的有效性，显示出其能够以更少的迭代次数收敛到最佳条件。这项研究突出了机器学习驱动方法在加速材料合成和优化过程中的潜力，为二维（2D）材料技术的进步铺平了道路。
