Active learning (AL) has the potential to accelerate materials discovery. Multi-principle Component Alloys (MPCAs) that maintain their mechanical properties at high temperatures are in high demand for aerospace and aeronautics applications. Coupling AL with physics-based simulations, we created an autonomous workflow to identify potential candidate materials. The workflow can be accessed here: https://nanohub.org/tools/activemeltheas

In this tool, we created a visualization dashboard for the explored MPCAs outlined in our publication:

Farache, D. E., Verduzco, J. C., McClure, Z. D., Desai, S., & Strachan, A. (2021). Active learning and molecular dynamics simulations to find high melting temperature alloys. arXiv preprint arXiv:2110.08136.

This dashboard allows for reproducibility of the results of our paper, in a simple low-dimensional representation of the high-dimensional space. 

Plotly, Ipywidgets

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

Researchers should cite this work as follows:

• Juan Carlos Verduzco Gastelum, Zachary D McClure, Alejandro Strachan (2023), "Visualization Dashboard for MPCAs," https://nanohub.org/resources/meltdashboard. (DOI: 10.21981/4MY4-9258).

  BibTex | EndNote

1. Active learning
2. alloys
3. interactive content
4. melting point
5. molecular dynamics (MD)
6. Multi-Principle Component Alloys (MPCAs)
7. scientific visualization
8. scientific workflows
9. Sim2L