Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation-Reference-Cited by-同舟云学术

Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation

Published:2016-01-19 Issue:2 Volume:12 Page:765-773
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Cuny Jérôme¹,Xie Yu²,Pickard Chris J.³,Hassanali Ali A.⁴

Affiliation:

1. Laboratoire de Chimie et Physique Quantiques (LCPQ), Université de Toulouse [UPS] and CNRS, 118 Route de Narbonne, F-31062 Toulouse, France

2. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States

3. Department of Materials Science & Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS, United Kingdom

4. Condensed Matter Physics Section, The Abdus Salaam International Center for Theoretical Physics, Strada Costiera 11, I-34151 Trieste, Trieste, Italy

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.5b01006

Reference99 articles.

1. MacKenzie, K. J. D.; Smith, M. E.Multinuclear Solid-State NMR of Inorganic Materials;Pergamon:Oxford, 2002; pp201–616.

2. Bakhmutov, V. I.Solid-State NMR in Materials Science: Principles and Applications;CRC Press: 2011; pp231–257.

3. Isotropic Spectra of Half-Integer Quadrupolar Spins from Bidimensional Magic-Angle Spinning NMR

4. Multiple-Quantum Magic-Angle Spinning NMR: A New Method for the Study of Quadrupolar Nuclei in Solids

Cited by 50 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. Dielectric Barrier Discharge Plasma-Enabled Energy Conversion Under Multiple Operating Parameters: Machine Learning Optimization;Plasma Chemistry and Plasma Processing;2023-12-13

2. Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks;The Journal of Physical Chemistry A;2023-03-02

3. The need for operando modelling of ²⁷Al NMR in zeolites: the effect of temperature, topology and water;Chemical Science;2023

4. Modelling complex molecular interactions in catalytic materials for energy storage and conversion in nuclear magnetic resonance;Frontiers in Catalysis;2022-07-19

5. Regression Machine Learning Models Used to Predict DFT-Computed NMR Parameters of Zeolites;Computation;2022-05-13