Multifidelity Machine Learning for Molecular Excitation Energies-Reference-Cited by-同舟云学术

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Multifidelity Machine Learning for Molecular Excitation Energies

Published:2023-10-20 Issue:21 Volume:19 Page:7658-7670
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Vinod Vivin¹²^ORCID,Maity Sayan³^ORCID,Zaspel Peter¹²^ORCID,Kleinekathöfer Ulrich³^ORCID

Affiliation:

1. School of Mathematics and Natural Science, University of Wuppertal, Wuppertal 42119, Germany

2. School of Computer Science and Engineering, Constructor University, Campus Ring 1, Bremen 28759, Germany

3. School of Science, Constructor University, Campus Ring 1, Bremen 28759, Germany

Funder

Deutsche Forschungsgemeinschaft

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.3c00882

Reference55 articles.

1. Lessons from nature about solar light harvesting

2. Atoms to Phenotypes: Molecular Design Principles of Cellular Energy Metabolism

3. Optical spectra in the condensed phase: Capturing anharmonic and vibronic features using dynamic and static approaches

4. The atomistic modeling of light-harvesting complexes from the physical models to the computational protocol

5. Recent progress in atomistic modeling of light-harvesting complexes: a mini review

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1. Multitask methods for predicting molecular properties from heterogeneous data;The Journal of Chemical Physics;2024-07-03

2. In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back;Advanced Materials;2024-05-25

3. Protein Effects on the Excitation Energies and Exciton Dynamics of the CP24 Antenna Complex;The Journal of Physical Chemistry B;2024-05-17

4. Optimized multifidelity machine learning for quantum chemistry;Machine Learning: Science and Technology;2024-03-01

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