1 code implementation • 16 Jul 2021 • Cole Miles, Matthew R. Carbone, Erica J. Sturm, Deyu Lu, Andreas Weichselbaum, Kipton Barros, Robert M. Konik
We employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions.
no code implementations • 9 Jun 2020 • Justin S. Smith, Nicholas Lubbers, Aidan P. Thompson, Kipton Barros
These forces provide much more information than the energy alone.
1 code implementation • 10 Mar 2020 • Justin S. Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton Barros
The accuracy and robustness of an ML potential is primarily limited by the quality and diversity of the training dataset.
no code implementations • 29 Sep 2017 • Nicholas Lubbers, Justin S. Smith, Kipton Barros
We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations.
Ranked #13 on Formation Energy on QM9
1 code implementation • 19 Feb 2017 • Bertrand Rouet-Leduc, Claudia Hulbert, Nicholas Lubbers, Kipton Barros, Colin Humphreys, Paul A. Johnson
Forecasting fault failure is a fundamental but elusive goal in earthquake science.
Geophysics
no code implementations • 8 Nov 2016 • Nicholas Lubbers, Turab Lookman, Kipton Barros
We use activations in a pre-trained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images.