Transfer Learning from Simulation to Experimental Data: NMR Chemical Shift Predictions-Reference-Cited by-同舟云学术

Transfer Learning from Simulation to Experimental Data: NMR Chemical Shift Predictions

Published:2021-04-07 Issue:14 Volume:12 Page:3662-3668
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Han Herim¹²^ORCID,Choi Sunghwan¹^ORCID

Affiliation:

1. Division of National Supercomputing, Korea Institute of Science and Technology Information, 245 Daehak-Ro, Yuseong-Gu, Daejeon 34141, Republic of Korea

2. Department of Polymer Science and Engineering, Dankook University, 152 Jukjeon-Ro, Suji-Gu, Yongin, Gyeonggi 16890, Republic of Korea

Funder

National Research Foundation of Korea

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.1c00578

Reference34 articles.

1. State of the art and perspectives in the application of quantum chemical prediction of 1 H and 13 C chemical shifts and scalar couplings for structural elucidation of organic compounds

2. Computational Prediction of 1H and 13C Chemical Shifts: A Useful Tool for Natural Product, Mechanistic, and Synthetic Organic Chemistry

3. Building blocks for automated elucidation of metabolites: Machine learning methods for NMR prediction

4. Rapid prediction of NMR spectral properties with quantified uncertainty

5. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

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