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Neural architectures that learn potential energy surfaces from molecular data have undergone fast improvement in recent years.
Cormorant: Covariant Molecular Neural Networks
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Semiempirical gga-type density functional constructed with a long-range dispersion correction
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Ewald methods for inverse power-law interactions in tridimensional and quasi-two-dimensional systems
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Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
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Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
Zhang, S., Liu, Y., and Xie, L · 2011
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Density functional theory: Its origins, rise to prominence, and future
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Ewald summation for molecular simulations
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Attention is All you Need
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Non-local graph neural networks
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
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Staacke, C. G., Heenen, H. H., Scheurer, C., Csányi, G., Reuter, K., and Margraf, J. T · 2021
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Crystal diffusion variational autoencoder for periodic material generation, 2021
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A generally applicable atomic-charge dependent london dispersion correction
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Limitations of empirical supercell extrapolation for calculations of point defects in bulk, at surfaces, and in two-dimensional materials
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