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Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science.
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A compact and accurate semi-global potential energy surface for malonaldehyde from constrained least squares regression
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Machine learning for quantum mechanical properties of atoms in molecules
Rupp, M., Ramakrishnan, R. & von Lilienfeld, O. A · 2015
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Gaussian Approximation Potentials: A brief tutorial introduction
Bartók, A. P. & Csányi, G · 2015
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Learning scheme to predict atomic forces and accelerate materials simulations
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Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces
Prediction errors of molecular machine learning models lower than hybrid DFT error
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A universal strategy for the creation of machine learning-based atomistic force fields
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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
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Machine learning of accurate energy-conserving molecular force fields
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Learning potential energy landscapes using graph kernels
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Use machine learning to find energy materials
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Li, Z., Kermode, J. R. & De Vita, A · 2015
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Schiavinato, M., Gasparetto, A. & Torsello, A · 2015
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Perspective: Machine learning potentials for atomistic simulations
Behler, J · 2016
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Comparing molecules and solids across structural and alchemical space
De, S., Bartok, A. P., Csányi, G. & Ceriotti, M · 2016
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On valid optimal assignment kernels and applications to graph classification
Kriege, N. M., Giscard, P.-L. & Wilson, R. C · 2016
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Visual Cortex and Deep Networks: Learning Invariant Representations (MIT Press, 2016)
Poggio, T. & Anselmi, F · 2016
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On invariance and selectivity in representation learning
Anselmi, F., Rosasco, L. & Poggio, T · 2016
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Psi4 1.1: An open-source electronic structure program emphasizing automation, advanced libraries, and interoperability
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High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium
Schran, C., Uhl, F., Behler, J. & Marx, D · 2018
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Solid harmonic wavelet scattering for predictions of molecule properties
Eickenberg, M., Exarchakis, G., Hirn, M., Mallat, S. & Thiry, L · 2018
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VAMPnets for deep learning of molecular kinetics
Mardt, A., Pasquali, L., Wu, H. & Noé, F · 2018
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Efficient nonparametric n-body force fields from machine learning
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Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics
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Convolutional neural networks for atomistic systems
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Outsmarting Quantum Chemistry Through Transfer Learning (2018)
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Amber 2018
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Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
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