Reducing the time and cost associated with the discovery of new materials with unprecedented properties is expected to have significant societal impact. New materials are needed in the fields of energy, transportation, aerospace, and medicine, among others. This challenge will use machine learning tools to reduce the number of experiments to achieve a design goal.

This challenge involves the use of active learning in the context of materials discovery online simulations. Active learning is a subset of machine learning where the information available at a given time is used to determine the next experiment to carry out in order to achieve a design goal. This challenge involves finding the alloy with the highest hardness in the lowest number of experiments.

Students will use a database of properties of a new class of metallic alloys, high entropy alloys, and will be tasked with designing an optimal active learning workflow. The challenge is to, starting from a common set of known materials, find the hardest alloy within the fewest number of experiments.

Researchers should cite this work as follows:

• Zachary D McClure, Alejandro Strachan (2021), "Active learning challenge for optimal material properties," https://nanohub.org/resources/activelearning. (DOI: 10.21981/VKNC-0Z82).

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