One of the most basic approaches to problem solving is to conceptualize the problem at different abstraction levels and translate from one abstraction level to the others easily, i.e., deal with them hierarchically. This concept is especially applicable to the field of novel materials discovery, wherein large candidate domains can be quickly and efficiently explored by hierarchically discarding irrelevant candidates. In this tutorial, we illustrate this approach using the example of wide band gap oxide perovskites. We will sequentially search a very large domain space of single and double oxide perovskites to identify candidates that are likely to be formable, thermodynamically stable, exhibit insulator nature and have a wide band gap. To this end, we will build four machine learning (ML) models: three classification and one regression model using experimental and DFT-calculated training data. The tutorial will discuss best practices for building ML models, commonly encountered pitfalls and how best to avoid them.

The corresponding tool for this tutorial can be run on nanoHUB at ML-aided High-throughput screening for Novel Oxide Perovskite Discovery.

Available for public release under LA-UR-21-27864.

Anjana Talapatra is Director's Postdoctoral Fellow in the Materials Science in Radiation and Dynamics Extremes Group at Los Alamos National Laboratory. Her research involves combining high-throughput Density Functional Theory (DFT) and Machine-Learning methods to accelerate the search for new materials. Previously, she was employed as a Research Scientist at the Materials Science & Engineering Department at Texas A&M university after graduating with a Ph.D from the Mechanical Engineering Department in 2015. While at Texas A&M Anjana also worked on a wide variety of materials-related problems using CALPHAD techniques, DFT and Machine-Learning.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Anjana Talapatra (2021), "A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery," https://nanohub.org/resources/35418.

  BibTex | EndNote

1. data-driven materials discovery
2. density functional theory (DFT)
3. hands on
4. machine learning
5. material discovery
6. materials science
7. NCN Group - Machine Learning Hands-on
8. oxides
9. perovskites
10. Perovskites Discovery Tool (perovMLdis)
11. regression
12. wide band gap