Consolidated Nuclear Waste Glass Database
Description
Incorporation of several physics-driven machine learning models to predict the properties of nuclear waste glass compositions – in addition, bootstrap other glass computational science models such as GlassPy and GlassNet to the database
Detailed example
Textual output; Chemical glass composition
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Develop an opensource, online database consisting of property information for nuclear waste glass data generated by various national laboratories over several decades
Audit / financial statement impact
In development stage and impact has not been determined
Controls / human review
ATO: Not reported; PIA: Not published