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Neural Networks for Computational Chemistry: Pitfalls and Recommendations

Published online by Cambridge University Press:  21 February 2013

Grégoire Montavon  and
Klaus-Robert Müller
Grégoire Montavon
Affiliation:
Machine Learning Group, TU Berlin, Marchstraße 23, 10587 Berlin, Germany
Klaus-Robert Müller
Affiliation:
Machine Learning Group, TU Berlin, Marchstraße 23, 10587 Berlin, Germany Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, South Korea
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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.

Keywords

compoundelectronic structuresimulation
Type
Articles
Information
MRS Online Proceedings Library (OPL) , Volume 1523: Symposium QQ – Materials Informatics , 2013 , mrsf12-1523-qq03-05
Copyright
Copyright © Materials Research Society 2013

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References

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