Cargando…

A Machine Learning Model of Chemical Shifts for Chemically and Structurally Diverse Molecular Solids

[Image: see text] Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR cryst...

Descripción completa

Detalles Bibliográficos
Autores principales:	Cordova, Manuel, Engel, Edgar A., Stefaniuk, Artur, Paruzzo, Federico, Hofstetter, Albert, Ceriotti, Michele, Emsley, Lyndon
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	American Chemical Society 2022
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9549463/ https://www.ncbi.nlm.nih.gov/pubmed/36237276 http://dx.doi.org/10.1021/acs.jpcc.2c03854

Ejemplares similares

Chemical shifts in molecular solids by machine learning
por: Paruzzo, Federico M., et al.
Publicado: (2018)

Bayesian probabilistic assignment of chemical shifts in organic solids
por: Cordova, Manuel, et al.
Publicado: (2021)

De Novo Crystal Structure Determination from Machine Learned Chemical Shifts
por: Balodis, Martins, et al.
Publicado: (2022)

Chemical Shift-Dependent Interaction Maps in Molecular Solids
por: Cordova, Manuel, et al.
Publicado: (2023)

Rapid Structure Determination of Molecular Solids Using Chemical Shifts Directed by Unambiguous Prior Constraints
por: Hofstetter, Albert, et al.
Publicado: (2019)

Homonuclear Decoupling in (1)H NMR of Solids by Remote Correlation
por: Moutzouri, Pinelopi, et al.
Publicado: (2020)

Pure Isotropic Proton NMR Spectra in Solids using Deep Learning
por: Cordova, Manuel, et al.
Publicado: (2023)

Structure determination of an amorphous drug through large-scale NMR predictions
por: Cordova, Manuel, et al.
Publicado: (2021)

Phase Segregation in Cs-, Rb- and K-Doped Mixed-Cation (MA)(x)(FA)(1–x)PbI(3) Hybrid Perovskites from Solid-State NMR
por: Kubicki, Dominik J., et al.
Publicado: (2017)

Analyzing Discrepancies in Chemical-Shift Predictions of Solid Pyridinium Fumarates
por: Dračínský, Martin
Publicado: (2021)

Dataset’s chemical diversity limits the generalizability of machine learning predictions
por: Glavatskikh, Marta, et al.
Publicado: (2019)

Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals
por: Ito, Kengo, et al.
Publicado: (2018)

Machine learning for chemical discovery
por: Tkatchenko, Alexandre
Publicado: (2020)

Temperature-Dependent Solid-State NMR Proton Chemical-Shift Values and Hydrogen Bonding
por: Malär, Alexander A., et al.
Publicado: (2021)

Solid-State (19)F NMR Chemical Shift in Square-Planar Nickel–Fluoride Complexes Linked by Halogen Bonds
por: Castro, Abril C., et al.
Publicado: (2023)

Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks
por: Venetos, Maxwell C., et al.
Publicado: (2023)

Machine learning in chemical reaction space
por: Stocker, Sina, et al.
Publicado: (2020)

Machine Learning Applications for Chemical Reactions
por: Park, Sanggil, et al.
Publicado: (2022)

Dynamic chemical processes on solid surfaces: chemical reactions and catalysis
por: Tanaka, Ken-ichi
Publicado: (2017)

Atomic-level structure determination of amorphous molecular solids by NMR
por: Cordova, Manuel, et al.
Publicado: (2023)

Structure elucidation of a complex CO(2)-based organic framework material by NMR crystallography
por: Leclaire, Julien, et al.
Publicado: (2016)

Observation of site-selective chemical bond changes via ultrafast chemical shifts
por: Al-Haddad, Andre, et al.
Publicado: (2022)

Influence of ligand encapsulation on cobalt-59 chemical-shift thermometry
por: Ozvat, Tyler M., et al.
Publicado: (2019)

Artificial intelligence: machine learning for chemical sciences
por: Karthikeyan, Akshaya, et al.
Publicado: (2021)

Applications of Machine Learning in Chemical and Biological Oceanography
por: Sadaiappan, Balamurugan, et al.
Publicado: (2023)

Machine Learning in Unmanned Systems for Chemical Synthesis
por: Wang, Guoqiang, et al.
Publicado: (2023)

Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical–Genetic Interactions
por: Safizadeh, Hamid, et al.
Publicado: (2021)

Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors
por: Durai, Prasannavenkatesh, et al.
Publicado: (2023)

Robustness of Local Predictions in Atomistic Machine Learning Models
por: Chong, Sanggyu, et al.
Publicado: (2023)

Applications of Chemical Shift Imaging to Marine Sciences
por: Lee, Haakil, et al.
Publicado: (2010)

DNA structures from phosphate chemical shifts
por: Abi-Ghanem, Joséphine, et al.
Publicado: (2010)

Ruptured Intracranial Dermoid and the Chemical Shift Artifact
por: Sadashiva, Nishanth, et al.
Publicado: (2016)

Chemical shift assignment of a thermophile frataxin
por: Rasheed, Masooma, et al.
Publicado: (2017)

Predicting chemical shifts with graph neural networks
por: Yang, Ziyue, et al.
Publicado: (2021)

Can quantum-chemical NMR chemical shifts be used as criterion for force-field development
por: Exner, Thomas E, et al.
Publicado: (2014)

Machine learning unifies the modeling of materials and molecules
por: Bartók, Albert P., et al.
Publicado: (2017)

(23)Na chemical shift imaging in the living rat brain using a chemical shift agent, Tm[DOTP](5–)
por: Bajwa, Awais A, et al.
Publicado: (2022)

The applications of machine learning to predict the forming of chemically stable amorphous solid dispersions prepared by hot-melt extrusion
por: Jiang, Junhuang, et al.
Publicado: (2023)

(7)Li NMR Chemical Shift Imaging To Detect Microstructural Growth of Lithium in All-Solid-State Batteries
por: Marbella, Lauren E., et al.
Publicado: (2019)

A Simple and Fast Approach for Predicting (1)H and (13)C Chemical Shifts: Toward Chemical Shift-Guided Simulations of RNA
por: Frank, Aaron T., et al.
Publicado: (2014)

Cannot write session to /tmp/vufind_sessions/sess_3cmq0g86rh4ski0ggfklqoh0ka