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Large scale Density Functional Theory (DFT) based electronic structure calculations are highly time consuming and scale poorly with system size.
New many-body potential for the bond order
Pettifor, D · 1989
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Tight-binding modelling of materials
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Environment-dependent tight-binding potential model
Wang, C. et al · 1997
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Self-consistent-charge density-functional tight-binding method for simulations of complex materials properties
Elstner, M. et al · 1998
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Si tight-binding parameters from genetic algorithm fitting
Klimeck, G. et al · 2000
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The elements of statistical learning , vol. 1 (Springer series in statistics Springer, Berlin, 2001)
Friedman, J., Hastie, T. & Tibshirani, R · 2001
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Compact expression for the angular dependence of tight-binding hamiltonian matrix elements
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Smola, A. J. & Schölkopf, B · 2004
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Gaussian processes for machine learning (2006)
Rasmussen, C. E · 2006
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, J. & Parrinello, M · 2007
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Wang, C.-Z. et al · 2008
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Qian, X. et al · 2008
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Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
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Parameterization of tight-binding models from density functional theory calculations
Urban, A., Reese, M., Mrovec, M., Elsässer, C. & Meyer, B · 2011
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Methods in electronic structure calculations
Bowler, D. & Miyazaki, T · 2012
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Fast and accurate modeling of molecular atomization energies with machine learning
Rupp, M., Tkatchenko, A., Müller, K.-R. & Von Lilienfeld, O. A · 2012
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Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R. & Burke, K · 2012
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Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
von Lilienfeld, O. A., Ramakrishnan, R., Rupp, M. & Knoll, A · 2015
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Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M. & Tucker, G. J · 2015
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Gaussian approximation potentials: A brief tutorial introduction
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Machine learning for quantum mechanics in a nutshell
Rupp, M · 2015
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Bartók, A. P., Kondor, R. & Csányi, G · 2013
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