  Multi-principal element alloys (MPEAs) are produced by combining metallic
elements in what is a diverse range of proportions. MPEAs reported to date have
revealed promising performance due to their exceptional mechanical properties.
Training a machine learning (ML) model on known performance data is a
reasonable method to rationalise the complexity of composition dependent
mechanical properties of MPEAs. This study utilises data from a specifically
curated dataset, that contains information regarding six mechanical properties
of MPEAs. A parser tool was introduced to convert chemical composition of
alloys into the input format of the ML models, and a number of ML models were
applied. Finally, Gradio was used to visualise the ML model predictions and to
create a user-interactive interface. The ML model presented is an initial
primitive model (as it does not factor in aspects such as MPEA production and
processing route), however serves as a an initial user tool, whilst also
providing a workflow for other researchers.
