Enhanced Predictions for the Experimental Photophysical Data Using the Featurized Schnet-Bondstep Approach-Reference-Cited by-同舟云学术

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Enhanced Predictions for the Experimental Photophysical Data Using the Featurized Schnet-Bondstep Approach

Published:2023-05-01 Issue:14 Volume:19 Page:4559-4567
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Hung Sheng-Hsuan¹,Ye Zong-Rong¹,Cheng Chi-Feng¹,Chen Berlin²,Tsai Ming-Kang¹³^ORCID

Affiliation:

1. Department of Chemistry, National Taiwan Normal University, Taipei 11677, Taiwan

2. Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 11677, Taiwan

3. Department of Chemistry, Fu-Jen Catholic University, New Taipei City 24205, Taiwan

Funder

National Science and Technology Council

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.3c00054

Reference45 articles.

1. Structural relationship between photophysical data of 1,3,5-trisubstituted oligoaryleneethynylene benzene star-shaped molecules and number of carbon atoms

2. Structure-Activity Relationship of Nerve-Highlighting Fluorophores

3. Predicting the emission wavelength of organic molecules using a combinatorial QSAR and machine learning approach

4. Molecular excited states through a machine learning lens

5. Data-driven modeling of S0 → S1 excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design

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1. Using interpretable machine learning techniques to analyze the thermal decomposition behavior of high-energy compounds for dataset partitioning;Proceedings of the 2024 5th International Conference on Computing, Networks and Internet of Things;2024-05-24

2. First-Principles Calculations of Excited-State Decay Rate Constants in Organic Fluorophores;The Journal of Physical Chemistry A;2023-11-21

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