Convolutional neural networks for atomistic systems

Abstract

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 cost of empirical potentials while maintaining the accuracy of \emph{ab initio} calculations. This method uses deep convolutional neural networks (CNNs), where the input to these networks are simple representations of the atomic structure. We use this approach to predict energies obtained using density functional theory (DFT) for 2D hexagonal lattices of various types. Using a dataset consisting of graphene, hexagonal boron nitride (hBN), and graphene-hBN heterostructures, with and without defects, we trained a deep CNN that is capable of predicting DFT energies to an extremely high accuracy, with a mean absolute error (MAE) of 0.198 meV / atom (maximum absolute error of 16.1 meV / atom). To explore our new methodology, we investigate the ability of a deep neural network (DNN) in predicting a Lennard-Jones energy and separation distance for a dataset of dimer molecules in both two and three dimensions. In addition, we systematically investigate the flexibility of the deep learning models by performing interpolation and extrapolation tests.

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• Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules
• Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning
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• On Machine Learning Force Fields for Metallic Nanoparticles
• Machine Learning Diffusion Monte Carlo Energies
• Efficient Learning of a One-dimensional Density Functional Theory
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• Supervised learning of random quantum circuits via scalable neural networks
• Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches
• On the Quality of Uncertainty Estimates from Neural Network Potential Ensembles
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