Explainable prediction of N-V-related defects in nanodiamond using neural networks and Shapley values
Date
2022-01-19
Authors
Barnard, Amanda
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Although the negatively charged nitrogen-vacancy (N-V ) defect in nanodiamonds is desirable for a variety of biomedical applications, a range of other defect complexes involving nitrogen and/or vacancies can also exist, depending on their relative stability. Using machine learning, a re-usable model is developed to predict the likelihood of a particular defect complex being stable at a given depth below reconstructed or hydrogen-passivated surfaces. A neural network is used to generate a system of equations that can be easily implemented in any workflow, and explainable artificial intelligence (XAI) methods are used to provide insights into which structural features and defect configurations are most responsible for the model prediction. It is found that, although the number of nitrogen atoms present in the defect is the most important feature determining the defect likelihood, the most influential data instances are the unlikely defects, providing a type of baseline for comparison.
Description
Keywords
Citation
Barnard, Explainable prediction of N-V-related defects in nanodiamond using neural networks and Shapley values, Cell Reports Physical Science (2021), https://doi.org/10.1016/j.xcrp.2021.100696
Collections
Source
Cell Reports Physical Science
Type
Journal article
Book Title
Entity type
Access Statement
Open Access
License Rights
Creative Commons BY-NC-ND license
Restricted until
Downloads
File
Description