工作流程
贝叶斯优化
自动化
材料科学
脉冲激光沉积
沉积(地质)
表征(材料科学)
薄膜
计算机科学
纳米技术
吞吐量
人工智能
工程类
机械工程
无线
生物
古生物学
数据库
电信
沉积物
作者
Sumner B. Harris,Arpan Biswas,Seok Joon Yun,Kevin M. Roccapriore,Christopher M. Rouleau,Alexander A. Puretzky,Rama K. Vasudevan,David B. Geohegan,Kai Xiao
出处
期刊:Small methods
[Wiley]
日期:2024-04-28
卷期号:8 (9): e2301763-e2301763
被引量:30
标识
DOI:10.1002/smtd.202301763
摘要
Abstract Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe 2 films. By combing the automation of PLD synthesis and in situ diagnostic feedback with a high‐throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe 2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD.
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