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Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology.
Self-consistent equations including exchange and correlation effects
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Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals
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Nanostructured high-entropy alloys with multiple principal elements: novel alloy design concepts and outcomes
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Microstructural development in equiatomic multicomponent alloys
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Generalized neural-network representation of high-dimensional potential-energy surfaces
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Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
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Fast and accurate modeling of molecular atomization energies with machine learning
Rupp, M., Tkatchenko, A., Müller, K.-R. & VonLilienfeld, O. A · 2012
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Machine learning of molecular electronic properties in chemical compound space
Montavon, G. et al · 2013
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On representing chemical environments
Bartók, A. P., Kondor, R. & Csányi, G · 2013
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Solution-phase epitaxial growth of noble metal nanostructures on dispersible single-layer molybdenum disulfide nanosheets
Huang, X. et al · 2013
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Metal contacts on physical vapor deposited monolayer MoS2
Gong, C. et al · 2013
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cp2k: atomistic simulations of condensed matter systems
Hutter, J., Iannuzzi, M., Schiffmann, F. & Vandevondele, J · 2014
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Influence of strain and metal thickness on metal-MoS2 contacts
Saidi, W. A · 2014
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Adam: a method for stochastic optimization
Kingma, D. & Ba, J · 2015
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Trends in the adsorption and growth morphology of metals on the MoS2(001) surface
Saidi, W. A · 2015
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Density functional theory study of nucleation and growth of pt nanoparticles on MoS2(001) surface
Saidi, W. A · 2015
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How van der Waals interactions determine the unique properties of water
Morawietz, T., Singraber, A., Dellago, C. & Behler, J · 2016
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ANI-1: an extensible neural network potential with dft accuracy at force field computational cost
Smith, J. S., Isayev, O. & Roitberg, A. E · 2017
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Intrinsic bond energies from a bonds-in-molecules neural network
Yao, K., Herr, J. E., Brown, S. N. & Parkhill, J · 2017
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Deep sets
Zaheer, M. et al · 2017
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Polymorphism in a high-entropy alloy
Zhang, F. et al · 2017
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Deep Potential: a general representation of a many-body potential energy surface
Han, J., Zhang, L., Car, R. & E, W · 2018
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Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics
Zhang, L., Han, J., Wang, H., Car, R. & E, W · 2018
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The Classical Groups: Their Invariants and Representations (Princeton university press, 2016)
Weyl, H · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E · 2017
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Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R. & Tkatchenko, A · 2017
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Machine learning of accurate energy-conserving molecular force fields
Chmiela, S. et al · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K. et al · 2017
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Machine learning unifies the modeling of materials and molecules
Bartók, A. P. et al · 2017
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DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
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Spherical CNNs
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Thermal expansion in dispersion-bound molecular crystals
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Experimentally validated interface structures of metal nanoclusters on MoS2
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Solving many-electron Schrödinger equation using deep neural networks
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