Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of WS2
Source abstract
Abstract
We applied Bayesian optimization (BO), a machine learning (ML) technique, to optimize the growth conditions of monolayer WS2 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.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: 2D Materials and Applications · MXene and MAX Phase Materials · Gas Sensing Nanomaterials and Sensors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan
Department of Chemistry
Nagoya University
Center for Basic Research on Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan
Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa City, Chiba 277-8568, Japan
Funding
Precursory Research for Embryonic Science and Technology · Japan Society for the Promotion of Science · Japan Science and Technology Corporation
References (37)
Show source-provided references
1. 10.1126/science.113720110.1126/science.1137201 ↗
2. 10.1038/s42254-021-00388-110.1038/s42254-021-00388-1 ↗
3. 10.1038/s41467-021-25688-z10.1038/s41467-021-25688-z ↗
4. 10.1016/j.mattod.2020.06.01210.1016/j.mattod.2020.06.012 ↗
5. 10.1002/adma.20210390710.1002/adma.202103907 ↗
6. 10.1109/TED.2018.286744110.1109/TED.2018.2867441 ↗
7. 10.1002/smll.20170009810.1002/smll.201700098 ↗
8. 10.1016/j.mssp.2022.10682910.1016/j.mssp.2022.106829 ↗
9. 10.1039/D2MA00091A10.1039/D2MA00091A ↗
10. 10.1002/smll.20200731210.1002/smll.202007312 ↗
11. 10.1039/D1NA00171J10.1039/D1NA00171J ↗
12. 10.1016/j.cossms.2024.10117610.1016/j.cossms.2024.101176 ↗
13. 10.1002/aelm.20230028110.1002/aelm.202300281 ↗
14. 10.1038/s41524-020-0280-210.1038/s41524-020-0280-2 ↗
15. 10.1002/adma.20200095310.1002/adma.202000953 ↗
16. 10.1021/jacs.0c0910510.1021/jacs.0c09105 ↗
17. 10.1146/annurev-matsci-070218-01001510.1146/annurev-matsci-070218-010015 ↗
18. 10.1016/j.compchemeng.2020.10683410.1016/j.compchemeng.2020.106834 ↗
19. Hey T. · 2009 · 1
20. 10.1021/acsnano.9b0422010.1021/acsnano.9b04220 ↗
21. 10.1016/j.mattod.2020.06.01010.1016/j.mattod.2020.06.010 ↗
22. 10.1016/j.md.2016.04.00110.1016/j.md.2016.04.001 ↗
23. 10.1038/s41586-021-03213-y10.1038/s41586-021-03213-y ↗
24. Ferreira T. · Imagej/fiji · 2012 · 1 · 155
25. Ichinose, N.; Maruyama, M.; Hotta, T.; Liu, Z.; Canton-Vitoria, R.; Okada, S.; Zeng, F.; Zhang, F.; Taniguchi, T.; Watanabe, K. Two-dimensional atomic-scale ultrathin lateral heterostructures, arXiv:2208.12696. arXiv.org e-Print archive, 2022. https://arxiv.org/abs/2208.12696.
26. 10.1002/advs.20200383410.1002/advs.202003834 ↗
27. 10.1021/acsami.1c1309610.1021/acsami.1c13096 ↗
28. 10.1016/j.mattod.2020.06.01210.1016/j.mattod.2020.06.012 ↗
29. 10.1002/adfm.20220319110.1002/adfm.202203191 ↗
30. 10.1016/j.matchemphys.2022.12718510.1016/j.matchemphys.2022.127185 ↗
31. Nguyen, V.; Schulze, S.; Osborne, M. In Bayesian Optimization for Iterative Learning, Advances in Neural Information Processing Systems; NeurIPS, 2020; pp 9361–9371.
32. 10.1016/j.mtphys.2020.10029610.1016/j.mtphys.2020.100296 ↗
33. 10.1002/adma.20210650610.1002/adma.202106506 ↗
34. 10.1016/j.matchemphys.2019.12259910.1016/j.matchemphys.2019.122599 ↗
35. 10.1002/adfm.20210533910.1002/adfm.202105339 ↗
36. 10.1021/acsaelm.1c0094610.1021/acsaelm.1c00946 ↗
37. Turner, R.; Eriksson, D.; McCourt, M.; Kiili, J.; Laaksonen, E.; Xu, Z.; Guyon, I. In Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020, NeurIPS 2020 Competition and Demonstration Track, PMLR, 2021; pp 3–26.