PIRSA:18040099

Deep models - hard problems made easy with deep learning

Tamblyn, I. (2018). Deep models - hard problems made easy with deep learning. Perimeter Institute. https://pirsa.org/18040099

Tamblyn, Isaac. Deep models - hard problems made easy with deep learning. Perimeter Institute, Apr. 16, 2018, https://pirsa.org/18040099 

          @misc{ pirsa_PIRSA:18040099,
            doi = {10.48660/18040099},
            url = {https://pirsa.org/18040099},
            author = {Tamblyn, Isaac},
            keywords = {Condensed Matter},
            language = {en},
            title = {Deep models - hard problems made easy with deep learning},
            publisher = {Perimeter Institute},
            year = {2018},
            month = {apr},
            note = {PIRSA:18040099 see, \url{https://pirsa.org}}
          }

Isaac Tamblyn

University of Ottawa

Talk number
PIRSA:18040099
Collection
Talk Type
Subject
Abstract

Recently, we have shown that deep neural networks can be used to solve the Schrödinger Equation (https://journals.aps.org/pra/abstract/10.1103/PhysRevA.96.042113), classical spin models (https://doi.org/10.1103/PhysRevE.97.032119), and 2d-materials such as graphene and boron-nitride (https://doi.org/10.1016/j.commatsci.2018.03.005).

I will argue that these "deep models" can be used to simulate and understand matter in a way which was not previously possible. Specifically, I will show how our recently reported extensive deep neural networks (https://arxiv.org/abs/1708.06686) can be used to infer the properties of meso-scale materials based on training data generated from much smaller structural motifs (evaluated using electronic structure methods such as density functional theory). Extensive deep neural networks scale as O(N) and can be efficiently evaluated in parallel using petascale computational platforms.