Nuclear Magnetic Resonance and Artificial Intelligence DOI Creative Commons
Stefan Kühn, Rômulo Pereira de Jesus, Ricardo M. Borges

et al.

Encyclopedia, Journal Year: 2024, Volume and Issue: 4(4), P. 1568 - 1580

Published: Oct. 18, 2024

This review explores the current applications of artificial intelligence (AI) in nuclear magnetic resonance (NMR) spectroscopy, with a particular emphasis on small molecule chemistry. Applications AI techniques, especially machine learning (ML) and deep (DL) areas shift prediction, spectral simulations, processing, structure elucidation, mixture analysis, metabolomics, are demonstrated. The also shows where progress is limited.

Language: Английский

Deep learning and its applications in nuclear magnetic resonance spectroscopy DOI
Yao Luo,

Xiaoxu Zheng,

Mengjie Qiu

et al.

Progress in Nuclear Magnetic Resonance Spectroscopy, Journal Year: 2025, Volume and Issue: 146-147, P. 101556 - 101556

Published: Jan. 17, 2025

Language: Английский

Citations

2
From Ab Initio to Instrumentation: A Field Guide to Characterizing Multivalent Liquid Electrolytes DOI
Glenn Pastel, Travis P. Pollard,

Oleg Borodin

et al.

Chemical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

In this field guide, we outline empirical and theory-based approaches to characterize the fundamental properties of liquid multivalent-ion battery electrolytes, including (i) structure chemistry, (ii) transport, (iii) electrochemical properties. When detailed molecular-scale understanding multivalent electrolyte behavior is insufficient use examples from well-studied lithium-ion electrolytes. recognition that coupling techniques highly effective, but often nontrivial, also highlight recent characterization efforts uncover a more comprehensive nuanced underlying structures, processes, reactions drive performance system-level behavior. We hope insights these discussions will guide design future studies, accelerate development next-generation batteries through modeling with experiments, help avoid pitfalls ensure reproducibility results.

Language: Английский

Citations

1
Identifying novel drug targets with computational precision DOI

Rutwij Dave,

P Giordano,

Sakshi Roy

et al.

Advances in pharmacology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
On the Use of Strong Proton Donors as a Tool for Overcoming Line Broadening in NMR: A Comment DOI Creative Commons
Pantelis Charisiadis,

Themistoklis Venianakis,

Christina Papaemmanouil

et al.

Magnetic Resonance in Chemistry, Journal Year: 2024, Volume and Issue: 63(3), P. 170 - 179

Published: Dec. 4, 2024

Overcoming line broadening of labile protons and achieving high-resolution NMR spectra is crucial for the structural conformational analysis organic molecules. Recently, Ma et al. (Magn. Reson. Chem. 2024, 62, 198-207) demonstrated effectiveness 2,2,2-trifluoroacetic acid (TFA) in sharpening signals nitrogen-containing compounds which exhibit prototropic tautomerization or isomerism using high molar ratio [acids]/[solute] ~ 5 to 200. In this commentary, we provide an overview earlier publications highlight extensive applications TFA enhancing resolution across a variety functional groups, with use very small ratios 10-3 10-2. The prospects unequivocal structure as starting point will be analyzed.

Language: Английский

Citations

3
Nuclear Magnetic Resonance and Artificial Intelligence DOI Creative Commons
Stefan Kühn, Rômulo Pereira de Jesus, Ricardo M. Borges

et al.

Encyclopedia, Journal Year: 2024, Volume and Issue: 4(4), P. 1568 - 1580

Published: Oct. 18, 2024

This review explores the current applications of artificial intelligence (AI) in nuclear magnetic resonance (NMR) spectroscopy, with a particular emphasis on small molecule chemistry. Applications AI techniques, especially machine learning (ML) and deep (DL) areas shift prediction, spectral simulations, processing, structure elucidation, mixture analysis, metabolomics, are demonstrated. The also shows where progress is limited.

Language: Английский

Citations

1