175 citations · 1.1k across the 47 of their papers we have counts for
10 papers · 1 filter
Automatic graph representation algorithm for heterogeneous catalysis
Zachary Gariepy, ZhiWen Chen, Isaac Tamblyn +2
One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of ML-aided exploration, m…
Deep Learning and Crystal Plasticity: A Preconditioning Approach for Accurate Orientation Evolution Prediction
Peyman Saidi, Hadi Pirgazi, Mehdi Sanjari +5
Efficient and precise prediction of plasticity by data-driven models relies on appropriate data preparation and a well-designed model. Here we introduce an unsupervised machine lea…
Interpretable discovery of new semiconductors with machine learning
Hitarth Choubisa, Petar Todorović, Joao M. Pina +5
Machine learning models of materials accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated resul…
Deep Learning and Density Functional Theory
Kevin Ryczko, David Strubbe, Isaac Tamblyn
We show that deep neural networks can be integrated into, or fully replace, the Kohn-Sham density functional theory scheme for multi-electron systems in simple harmonic oscillator…
Energy Level Alignment at Hybridized Organic-metal Interfaces: the Role of Many-electron Effects
Yifeng Chen, Isaac Tamblyn, Su Ying Quek
Hybridized molecule/metal interfaces are ubiquitous in molecular and organic devices. The energy level alignment (ELA) of frontier molecular levels relative to the metal Fermi leve…
Deep neural networks for direct, featureless learning through observation: the case of 2d spin models
Kyle Mills, Isaac Tamblyn
We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4x4 Ising model. Using its success at this task, we motivate t…
Convolutional neural networks for atomistic systems
Kevin Ryczko, Kyle Mills, Iryna Luchak +2
We introduce a new method, called CNNAS (convolutional neural networks for atomistic systems), for calculating the total energy of atomic systems which rivals the computational cos…
Deep learning and the Schrödinger equation
Kyle Mills, Michael Spanner, Isaac Tamblyn
We have trained a deep (convolutional) neural network to predict the ground-state energy of an electron in four classes of confining two-dimensional electrostatic potentials. On ra…
Structural characterizations of water-metal interfaces
Kevin Ryczko, Isaac Tamblyn
We analyze and compare the structural, dynamical, and electronic properties of liquid water next to prototypical metals including Pt, graphite, and graphene. Our results are built…
Molecular Adsorption on Metal Surfaces with a van der Waals Density Functional
Guo Li, Isaac Tamblyn, Valentino R. Cooper +2
The adsorption of 1,4-benzenediamine (BDA) on the Au(111) surface and azobenzene on the Ag(111) surface is investigated using density functional theory (DFT) with a non-local densi…