(156c) Using Artificial Intelligence to Discover New Materials: The Role of Material Representations

Rational, data-driven materials discovery has the potential to make research and development efforts far faster and cheaper. In such a paradigm, computer models trained to find patterns in massive chemical datasets would rapidly scan compositions and systematically identify attractive candidates. Here, we present several examples of our work on developing machine learning (ML) and deep learning methods capable of creating predictive models using a diverse range of materials data. We illustrate three distinct materials representations and compare the resulting ML models: 1) composition-only attributes, 2) crystal structure-based attributes based on Voronoi tesselation, and 3) an improved version of the crystal graph convolutional neural net (iCGCNN). We also examine the use of “representation-free” approaches based on neural net deep learning. As input training data, we demonstrate ML on both large computational datasets of DFT calculations, as implemented in the Open Quantum Materials Database (oqmd.org), and also experimental databases of materials properties. We construct ML models using a large and chemically diverse list of attributes, which we demonstrate can be used as an effective tool to automatically learn intuitive design rules, predict diverse properties of crystalline and amorphous materials, such as formation energy, specific volume, band gap energy, and glass-forming ability, and accelerate combinatorial searches.