arXiv subjects

Kochise C. Bennett

Publications and source records attributed to Kochise C. Bennett.

3 recordsLinked to original sources

arXiv2020

Strong Anisotropy in Liquid Water upon Librational Excitation using Terahertz Laser Fields

Tracking the excitation of water molecules in the homogeneous liquid is challenging due to the ultrafast dissipation of rotational excitation energy through the hydrogen-bonded network. Here we demonstrate strong transient anisotropy of liquid water through librational excitation using single-color pump-probe experiments at 12.3 THz. We deduce a third order response of chi^3 exceeding previously reported values in the optical range by three orders of magnitude. Using a theory that replaces the nonlinear response with a material response property amenable to molecular dynamics simulation, we show that the rotationally damped motion of water molecules in the librational band is resonantly driven at this frequency, which could explain the enhancement of the anisotropy in the liquid by the external Terahertz field. By addition of salt (MgSO4), the hydration water is instead dominated by the local electric field of the ions, resulting in reduction of water molecules that can be dynamically perturbed by THz pulses.

physics.chem-ph↗

arXiv2019

Accurate Prediction of Chemical Shifts for Aqueous Protein Structure for "Real World" Cases using Machine Learning

Accurate prediction of NMR chemical shifts can in principle help refine aqueous solution structure of proteins to the quality of X-ray structures. We report a new machine learning algorithm for protein chemical shift prediction that outperforms existing chemical shift calculators on realistic NMR solution data. Our UCBShift predictor implements two modules: a transfer prediction module that employs both sequence and structural alignment to select reference candidates for experimental chemical shift replication, and a redesigned machine learning module based on random forest regression which utilizes more, and more carefully curated, feature extracted data. When combined together, this new predictor achieves state of the art accuracy for predicting chemical shifts on a "real-world" dataset, with root-mean-square errors of 0.31 ppm for amide hydrogens, 0.19 ppm for Halpha, 0.87 ppm for C, 0.81 ppm for Calpha, 1.01 ppm for Cbeta, and 1.83 ppm for N, exceeding current prediction accuracy of popular chemical shift predictors such as SPARTA+ and SHIFTX2.

physics.chem-ph↗

arXiv2019

A Multi-Resolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography

We have developed a deep learning algorithm for chemical shift prediction for atoms in molecular crystals that utilizes an atom-centered Gaussian density model for the 3D data representation of a molecule. We define multiple channels that describe different spatial resolutions for each atom type that utilizes cropping, pooling, and concatenation to create a multi-resolution 3D-DenseNet architecture (MR-3D-DenseNet). Because the training and testing time scale linearly with the number of samples, the MR-3D-DenseNet can exploit data augmentation that takes into account the property of rotational invariance of the chemical shifts, thereby also increasing the size of the training dataset by an order of magnitude without additional cost. We obtain very good agreement for 13C, 15N, and 17O chemical shifts, with the highest accuracy found for 1H chemical shifts that is equivalent to the best predictions using ab initio quantum chemistry methods.

physics.chem-ph↗