arXiv subjects

Darian Smalley

Publications and source records attributed to Darian Smalley.

3 recordsLinked to original sources

arXiv2023

Detecting Atomic Scale Surface Defects in STM of TMDs with Ensemble Deep Learning

Atomic-scale defect detection is shown in scanning tunneling microscopy images of single crystal WSe2 using an ensemble of U-Net-like convolutional neural networks. Standard deep learning test metrics indicated good detection performance with an average F1 score of 0.66 and demonstrated ensemble generalization to C-AFM images of WSe2 and STM images of MoSe2. Defect coordinates were automatically extracted from defect detections maps showing that STM image analysis enhanced by machine learning can be used to dramatically increase sample characterization throughput.

cond-mat.mtrl-sci↗

arXiv2023

Impact of thermal annealing on the interaction between monolayer MoS2 and Au

We have investigated the impact of thermal annealing on the interaction of single layer MoS2 and Au using Raman Spectroscopy. We found MoS2 has two main modes of interactions with the underlying Au being either weakly-coupled or strongly-coupled. The regions strongly-coupled to Au are hybridized to Au, minimally strained, and electron-doped. The weakly-coupled regions are found to be slightly hole-doped with tensile strain of 1.0 %. The observed nanoscale inhomogeneities in doping would result in Au contacts having a large variability in performance. The overall areal coverage of the strongly-coupled regions is not increased by thermal annealing, and the variability in the degree of hybridization increases at annealing temperatures above 100 °C. Our data also show that monolayer MoS2 starts to decouple from Au around 100 °C, becoming fully decoupled above 250 °C, suggesting that monolayer MoS2 produced by Au-assisted mechanical exfoliation may be more easily transferred off Au at elevated temperatures.

cond-mat.mtrl-sci↗

arXiv2022

Autonomous Investigations over WS$_2$ and Au{111} with Scanning Probe Microscopy

Individual atomic defects in 2D materials impact their macroscopic functionality. Correlating the interplay is challenging, however, intelligent hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible solution to this technically difficult and time consuming problem. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for spectral identification. Acquired data enable defect segmentation, and a workflow is provided for machine-driven decision making during experimentation with capability for user customization. We provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Hyperspectral investigations on WS$_2$ sulfur vacancy sites are explored, which is combined with local density of states confirmation on the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS$_2$, Au face-centered cubic, and Au hexagonal close packed regions are examined and detected by machine learning methods to demonstrate the potential of artificial intelligence for hyperspectral STS mapping.

cond-mat.mtrl-sci↗