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Molecular Electronics Artificial Neural Networks (MEANN) adapts recurrent neural networks to analyze single molecular break junction data. The project aims to improve data analysis, enhance reproducibility, and bridge gaps between theory and experiment in molecular physics.
Molecular Electronics Artificial Neural Networks (MEANN) will adapt for the first time a recurrent neural network (RNN) to address complex multivariate correlation questions that arise in single molecular break junction (SMBJ) experiments.
The hypothesis is that a RNN will be better than a human at identifying relationships between nanoscopic geometry changes of the junctions and the measured variables in SMBJ data sets, with little or no human bias.
These improvements in the data analysis appro…
KOBENHAVNS UNIVERSITET
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