Feature Extraction Using Semi-Supervised Deep Learning.

Features are defined as measurable properties that characterize observed phenomena and represent a key part of machine learning (ML) algorithms. In materials sciences, ML has successfully accelerated atomistic simulations using man-engineered features for tasks such as energy or atomic forces predictions. These features fulfill physics constraints such as rotational and translational invariance, uniqueness and, locality (the sum of local contributions reconstructs a global quantity). However, these ML models are known to perform poorly when operating out of the training set regime because features are not representative of the underlying structure of the data. This could be improved if features are extracted with advanced hybrid architectures e.g. a variational autoencoder that is trained with physics constraints introduced with an external task and a loss function. We will explore how the use of semi-supervised learning techniques can be a powerful tool for the extraction of features for atomistic simulations. All results shown herein can be reproduced with ML4Chem: a free software package for machine learning in chemistry and materials sciences.