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Found 3 papers, 1 papers with code
Understanding Anharmonic Effects on Hydrogen Desorption Characteristics of Mg$_n$H$_{2n}$ Nanoclusters by ab initio trained Deep Neural Network
In order to increase the size of NPs toward experiments of hydrogen desorption from MgH$_2$ we devise a computationally effective Machine Learning model trained to accurately determine the forces and total energies (i. e. the potential energy surfaces), integrating the latter with the SSCHA model to fully include the anharmonic effects.
Expeditious computation of nonlinear optical properties of arbitrary order with native electronic interactions in the time domain
We adapted a recently proposed framework to characterize the optical response of interacting electrons in solids in order to expedite its computation without compromise in accuracy at the microscopic level.
Materials Science Strongly Correlated Electrons
Mean Field Theory of Activation Functions in Deep Neural Networks
We present a Statistical Mechanics (SM) model of deep neural networks, connecting the energy-based and the feed forward networks (FFN) approach.
