A Bayesian approach to NMR crystal structure determination.

  title={A Bayesian approach to NMR crystal structure determination.},
  author={Edgar A. Engel and Andrea Anelli and Albert Hofstetter and Federico M. Paruzzo and Lyndon Emsley and M. Ceriotti},
  journal={Physical chemistry chemical physics : PCCP},
Nuclear Magnetic Resonance (NMR) spectroscopy is particularly well suited to determine the structure of molecules and materials in powdered form. Structure determination usually proceeds by finding the best match between experimentally observed NMR chemical shifts and those of candidate structures. Chemical shifts for the candidate configurations have traditionally been computed by electronic-structure methods, and more recently predicted by machine learning. However, the reliability of the… 

De Novo Crystal Structure Determination from Machine Learned Chemical Shifts

Determination of the three-dimensional atomic-level structure of powdered solids is one of the key goals in current chemistry. Solid-state NMR chemical shifts can be used to solve this problem, but

Atomic-resolution chemical characterization of (2x)72-kDa tryptophan synthase via four- and five-dimensional 1H-detected solid-state NMR

It is shown that assignments for the (2x)72-kDa protein tryptophan synthase can be achieved via higher-dimensional, proton-detected, solid-state NMR using a single, 1-mg, uniformly labeled, microcrystalline sample, granting access to atom-specific characterization of chemical properties and relaxation for the backbone and side chains, including those residues important for the catalytic turnover.

Imaging active site chemistry and protonation states: NMR crystallography of the tryptophan synthase α-aminoacrylate intermediate

A joint solid-state NMR, X-ray crystallography, and first-principles computational approach is made use to characterize the α-aminoacrylate intermediate in tryptophan synthase, a defining species for pyridoxal-5′-phosphate-dependent enzymes on the β-elimination and replacement pathway.

NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations

Floating chirality restrained distance geometry (fc-rDG) calculations are used to directly evolve structures from NMR data such as NOE-derived intramolecular distances or anisotropic residual dipolar

Ab Initio Machine Learning in Chemical Compound Space

While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics.

Revving up 13C NMR shielding predictions across chemical space: benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules

The requirement for accelerated and quantitatively accurate screening of nuclear magnetic resonance spectra across the small molecules chemical compound space is two-fold: (1) a robust ‘local’

Solid-state NMR spectroscopy.

This Primer summarizes the basic principles of NMR as applied to the wide range of solid systems, and describes the most common MAS NMR experiments and data analysis approaches for investigating biological macromolecules, organic materials, and inorganic solids.



Gaussian Processes for Machine Learning

The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.


  • Chem. Chem. Phys. 18, 21686
  • 2016

Nature Communications 9

  • 4501
  • 2018

Journal of the American Chemical Society 135

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  • 2013

Physical Chemistry Chemical Physics 21

  • 14992
  • 2019

Journal of the American Chemical Society t

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  • 2019

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  • Physics, Chemistry
: The hydrated electron  the species that results from the addition of a single excess electron to liquid water  has been the focus of much interest both because of its role in radiation chemistry

Improving the accuracy of solid-state nuclear magnetic resonance chemical shift prediction with a simple molecular correction.

It is demonstrated that a correction to the GGA result calculated on an isolated molecule at a higher level of theory significantly improves the correlations between experimental and calculated chemical shifts while adding almost no additional computational cost.

Acta Crystallographica B, Cに投稿される方へ