 This paper proposes a new deep learning architecture called branch residual learning, barnet. It uses fully connected layers to build accurate models for predicting material properties from numerical vectors representing different compositions. Barnet is more accurate than other deep learning models and requires fewer parameters, resulting in faster training and faster convergence. This article was authored by Vishig Gupta, Alec Peltikian, Weik & Liao, and others.