Data · dataset · 2015

Machine learning for exciton dynamics: Qy trajectories

Listed in DataCite

In this folder we report the data of our paper, recently posted on the arXiv.

Description

In particular we provide the energy gap trajectories for each BChl of monomer A of the Fenna-Matthews-Olson (FMO) complex. These were computed using QM/MM and TDDFT, and then they were predicted using multi layer perceptrons with different methods to select the training data ( Random, Correlation, Frobenius and Taxicab).

Topics

Stated by source: Mathematics
From keywords: Mathematics & Statistics · Statistics

Provenance · 1 source records, 10 field assertions

Source	Key	Last seen	Raw
DataCite	10.6084/m9.figshare.1611141	15 d ago	JSON v1

Field	Assertion	Extractor	Evidence
access_level	source · DataCite	connector:datacite@1.0.0	/data/attributes/rightsList
byte_size	source · DataCite	connector:datacite@1.0.0
concepts[field].fos:mathematics	source · DataCite	connector:datacite@1.0.0
concepts[field].local:field:mathematics-statistics	mapping · DataCite	vocabulary-mapper@1.0.0	keywords['Statistics']
created_date	source · DataCite	connector:datacite@1.0.0
description	source · DataCite	connector:datacite@1.0.0	/data/attributes/descriptions
license	source · DataCite	connector:datacite@1.0.0	/data/attributes/rightsList
publication_date	source · DataCite	connector:datacite@1.0.0	/data/attributes/dates
title	source · DataCite	connector:datacite@1.0.0	/data/attributes/titles/0/title
updated_date	source · DataCite	connector:datacite@1.0.0

Machine learning for exciton dynamics: Qy trajectories

Description

Links

Where it is published

Documentation and papers

Topics