摘要
Machine-learning(ML)techniques hold the potential of enabling efficient quantitative micrograph analysis,but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated.We collected thousands of scanning electron microscopy(SEM)micrographs for molecular solid materials,in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions.We then built ML models to predict the ultimate compressive strength(UCS)of consolidated molecular solids,by encoding micrographs with different image feature descriptors and training a random forest regressor,and by training an end-to-end deep-learning(DL)model.Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way.As a remedy,we explored intensity normalization techniques.It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness,but microscope-induced intensity variations can be difficult to eliminate.
基金
The authors would like to thank Donald Loveland,Jize Zhang,and Piyush Karande for prototype codes and helpful discussions.This work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344
was supported by the LLNL-LDRD Program under Project No.19-SI-001。
