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Neural Networks for Computational Chemistry: Pitfalls and Recommendations

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

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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MRS Online Proceedings Library (OPL)
  • ISSN: -
  • EISSN: 1946-4274
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