175 citations · 362 across the 9 of their papers we have counts for
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cond-mat.mtrl-sci2017★ 25 cited
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…
cond-mat.mtrl-sci2017★ 50 cited
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…
cond-mat.mtrl-sci2017★ 175 cited
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…