Dr. Alpha Lee delivered an A3MD Distinguished Seminar entitled: “Accelerating the materials design-make-test cycle with machine learning and coarse graining”
Unlike molecular chemistry, where valance-bond theory provides a robust framework to represent molecules as a graph, representing inorganic materials is significantly more intricate. On the one hand, representing a material using composition alone removes the possibility of identifying polymorphs. On the other hand, a representation based on distances between atoms is challenging to deploy in a computational high throughput screening workflow for novel materials because it is a priori unclear whether a particular composition/structure is thermodynamically stable. In my talk, I will discuss our journey in applying concepts in coarse graining to devise machine learning models for materials properties prediction and materials synthesis prediction. I will first discuss the lowest level of coarse graining – stoichiometry – and discuss a representation learning framework that predicts materials properties using stoichiometry as input. I will then show how this framework can be extended to predict the outcomes of materials synthesis, whilst also providing an interpretable “reaction similarity” metric that enables rapid search for literature precedents in materials synthesis. Finally, I will move up the coarse graining ladder and discuss how we can incorporate structure, albeit in a coarse-grained way, using the concept of Wyckoff representation. This approach allows us to effectively amortise crystal structure searching, turning an infinite search space into a combinatorially enumerable search problem.