Machine learning driven synthesis of few-layered WTe2
arXiv:1910.04603 · doi:10.1021/jacs.1c06786
Abstract
Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for the further study. Traditional exploration of the optimal synthesis conditions of novel materials is based on the trial-and-error approach, which is time consuming, costly and laborious. Recently, machine learning (ML) has demonstrated promising capability in guiding material synthesis through effectively learning from the past data and then making recommendations. Here, we report the implementation of supervised ML for the chemical vapor deposition (CVD) synthesis of high-quality 1D few-layered WTe2 nanoribbons (NRs). The synthesis parameters of the WTe2 NRs are optimized by the trained ML model. On top of that, the growth mechanism of as-synthesized 1T' few-layered WTe2 NRs is further proposed, which may inspire the growth strategies for other 1D nanostructures. Our findings suggest that ML is a powerful and efficient approach to aid the synthesis of 1D nanostructures, opening up new opportunities for intelligent material development.
References in corpus (17)
- XGBoost: A Scalable Tree Boosting System
- A Unified Approach to Interpreting Model Predictions
- Machine learning phases of matter
- Edge conduction in monolayer WTe2
- On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
- Quantum engineering of transistors based on 2D materials heterostructures
- Vapor-Liquid-Solid Growth of Monolayer MoS2 Nanoribbons
- Electrically switchable Berry curvature dipole in the monolayer topological insulator WTe2
- Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing
- Imaging Quantum Spin Hall Edges in Monolayer WTe2
- On the quantum spin Hall gap of monolayer 1T'-WTe2
- Machine learning-guided synthesis of advanced inorganic materials
- Unconventional charge-spin conversion in Weyl-semimetal WTe2
- One-dimensional edge contacts to a monolayer semiconductor
- Manipulating Weyl quasiparticles by orbital-selective photoexcitation in WTe2
- Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges
- Autonomous Scanning Probe Microscopy in-situ Tip Conditioning through Machine Learning