.png){kind=icon}
返回
Interpretable Machine Learning Approach for Identifying the Tip Sharpness in Atomic Force Microscopy
.png){kind=icon}
PRE
AI
DOI：10.1016/j.scriptamat.2022.114965.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Atomic force microscopy (AFM) is routinely used with indentation techniques to characterize the plastic deformation of materials. The accurate quantification of the features associated with the indent, which is used to quantify the hardness and indentation deformation mechanisms, depends on the sharpness of the AFM tip used for imaging. However, identifying the tip-sharpness of an atomic force microscope requires non-trivial mea-surements. Here, using machine learning, we develop a model to predict the tip sharpness of the AFM cantilever directly from the indent images. Further, we employ explainable machine learning models, such as integrated gradients and gradient shap, to interpret the features learned by the model. Altogether, we show that machine learning approaches can accelerate experiments by providing non-trivial information about the instrument performance, thereby enabling researchers to perform better quality experiments.
Keyword:
Atomic force microscopy
Nanoindentation
SqueezeNet
Gradient SHAP
Integrated gradients
Convolutional neural networks glasses
期刊
IF:
5.6
论文数:
1.6W
被引数:
5.1W
机构
引用论文
.png){kind=icon}
分享收藏An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments使用载荷和位移传感压痕实验确定硬度和弹性模量的改进技术
PREAI
分享收藏
查看更多内容