Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra-Reference-Cited by-同舟云学术

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

Published:2019-01-29 Issue:9 Volume:6 Page:1801367
ISSN:2198-3844
Container-title:Advanced Science
language:en
Short-container-title:Adv. Sci.

Author:

Ghosh Kunal¹²,Stuke Annika²,Todorović Milica²,Jørgensen Peter Bjørn³,Schmidt Mikkel N.³,Vehtari Aki¹,Rinke Patrick²⁴^ORCID

Affiliation:

1. Department of Computer ScienceAalto University P.O. Box 15400 Aalto FI‐00076 Finland

2. Department of Applied PhysicsAalto University P.O. Box 11100 Aalto FI‐00076 Finland

3. Department of Applied Mathematics and Computer ScienceTechnical University of Denmark Richard Petersens Plads, 2800 Kgs. Lyngby Denmark

4. Chair for Theoretical Chemistry and Catalysis Research CenterTechnische Universität München Lichtenbergstr. 4, D‐85747 Garching Germany

Funder

Horizon 2020 Framework Programme

Academy of Finland

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.201801367

Reference58 articles.

1. Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

2. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

3. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

4. Machine learning of molecular electronic properties in chemical compound space

Cited by 202 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Optical screening of citrus leaf diseases using label-free spectroscopic tools: A review;Journal of Agriculture and Food Research;2024-12

2. High performance filtering and high-sensitivity concentration retrieval of methane in photoacoustic spectroscopy utilizing deep learning residual networks;Photoacoustics;2024-10

3. Predicting Model for Device Density of States of Quantum-Confined SiC Nanotube with Magnetic Dopant: An Integrated Approach Utilizing Machine Learning and Density Functional Theory;Silicon;2024-09-10

4. Integration of Programmable Diffraction with Digital Neural Networks;ACS Photonics;2024-08-12

5. On the relevance of hyperparameter optimization of convolutional neural networks for retrieving spectral signals;Optics Continuum;2024-08-06