TY - GEN
T1 - Neural networks for computational chemistry
T2 - 2012 MRS Fall Meeting
AU - Montavon, Grégoire
AU - Müller, Klaus Robert
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84900309094&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84900309094&partnerID=8YFLogxK
U2 - 10.1557/opl.2013.189
DO - 10.1557/opl.2013.189
M3 - Conference contribution
AN - SCOPUS:84900309094
SN - 9781632661135
T3 - Materials Research Society Symposium Proceedings
SP - 24
EP - 29
BT - Materials Informatics
PB - Materials Research Society
Y2 - 25 November 2012 through 30 November 2012
ER -