Student Posters Repository

The field of computational materials science relies heavily on electronic structure methods like Density Functional Theory (DFT) for material characterization and design. Although these methods have had great success, their algorithmic asymptotic scalings limit the system sizes that can be studied. Recently, deep learning has had great success applied to industrial problems like computer vision and speech recognition. In our work, we show how one can use a deep neural network to calculate total energy predictions that rival the accuracy of DFT but at a fraction of the computational cost. We then describe a new deep neural network topology we call extensive deep neural networks. These neural networks can be used to calculate extensive properties of large scale atomistic systems with O(N) scaling (N is the number of unit cells in the system). We describe the structure of these networks and their applicability to 2D hexagonal materials.