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Sönke Lorenz , Axel Groß , and Matthias Scheffler . Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks. Chemical Physics Letters, 395(4–6):210–215, 2004.
Sergei Manzhos and Tucker Carrington . A random-sampling high dimensional model representation neural network for building potential energy surfaces. J. Chem. Phys., 125:084109, 2006.
Jörg Behler . Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Physical Chemistry Chemical Physics, 13(40):17930–17955, 2011.
John C. Snyder , Matthias Rupp , Katja Hansen , Klaus-Robert Müller , and Kieron Burke . Finding Density Functionals with Machine Learning, Physical Review Letters, 108(25): 253002, , 2012.
Matthias Rupp , Alexandre Tkatchenko , Klaus-Robert Müller , and O. Anatole von Lilienfeld . Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, Physical Review Letters, 108(5):058301, 2012.
David E. Rumelhart , Geoffrey E. Hinton , and Ronald J. Williams . Learning representations by backpropagating errors, Nature, 323, 533–536, 1986.
Lorenz C. Blum and Jean-Louis Reymond . 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. Journal of the American Chemical Society, 131(25):8732–8733, 2009.