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Neural Networks for Computational Chemistry: Pitfalls and Recommendations

  • Grégoire Montavon (a1) and Klaus-Robert Müller (a1) (a2)

There is a long history of using neural networks for function approximation in computational physics and chemistry. Despite their conceptual simplicity, the practitioner may face difficulties when it comes to putting them to work. This small guide intends to pinpoint some neural networks pitfalls, along with corresponding solutions to successfully realize function approximation tasks in physics, chemistry or other fields.

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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):210215, 2004.

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Jörg Behler . Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Physical Chemistry Chemical Physics, 13(40):1793017955, 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, American Physical Society, 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, 533536, 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):87328733, 2009.

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MRS Online Proceedings Library (OPL)
  • ISSN: -
  • EISSN: 1946-4274
  • URL: /core/journals/mrs-online-proceedings-library-archive
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