Active learning (AL) can drastically accelerate materials discovery and its power has been shown for a variety of materials and materials properties. In this tool, we couple active learning wiith molecular dynamics simulations to identify multiple principal component alloys (MPCAs) with high melting temperatures. We present a fully autonomous workflow for the efficient exploration of the high dimensional compositional space of MPCAs.

Lolopy

This effort was supported by the US National Science Foundation (DMREF-1922316). We acknowledge computational resources from nanoHUB and Purdue University through the Networkfor Computational Nanotechnology.

Researchers should cite this work as follows:

• Juan Carlos Verduzco Gastelum, David Enrique Farache, Zachary D McClure, Saaketh Desai, Alejandro Strachan (2021), "Active Learning Workflow for MPCAs," https://nanohub.org/resources/activemeltheas. (DOI: 10.21981/NK7E-HA16).

  BibTex | EndNote

1. Active learning
2. melting temperature
3. molecular dynamics (MD)
4. Multi-Principle Component Alloys (MPCAs)
5. scientific workflows