Publications by Year: Submitted

Submitted
Trevor David Rhone, Bethany Lusch, Marios Mattheakis, Dylan Sheils, Romakanta Bhattarai, Daniel T. Larson, Yoshiharu Krockenberger, and Efthimios Kaxiras. Submitted. “: Semi-supervised learning for materials discovery”.Abstract
We present an artificial intelligence implementation of semi-supervised
learning that is designed to accelerate materials discovery. The framework is composed
of an autoencoder neural network architecture, which learns a low-dimensional latent
representation of the input materials descriptors, and a multilayer feed-forward neural
network trained to map the latent representation to material properties, such as the
formation energy and magnetic moment. The framework learns a representation that
is appropriate for predicting these properties. Furthermore, because unlabeled data
can be used to improve the representation learned by the autoencoder, this coupled
structure is ideally suited to tackle learning tasks that are challenging due to a sparsity
of labeled data. As a concrete example, we study the performance of this approach
when applied to the search for novel two-dimensional magnetic materials.