Deepansh Srivastava

Postdoctoral research scientist

Ohio State University

About me

Result-driven research scientist specializes in developing new and improving existing methods in solid-state magnetic resonance for better understanding and characterizing of materials, both amorphous and crystalline, and bio-macromolecules. Recent work focuses on structural elucidation of materials from the 2D NMR spectrum. I am also a passionate developer of high throughput numerical libraries and software for simulating, processing, and analyzing scientific data using a multitude of tools, including statistical learning, linear-inversions, and machine learning. Proficient in C, python, and javascript programming languages, open to others as needed.

Interests

ssNMR of materials
Statistical/machine-learning methods to solve and analyse NMR of materials.
Spectral/Structural analysis of materials.
Library development for NMR spectral analysis.

Education

PhD in Chemistry, 2018

Ohio State University
BS-MS in Chemistry, 2013

IISER Mohali

Projects

mrinversion

Statistical learning-based linear inversion of 2D isotropic/anisotropic NMR correlation spectrum into 3D tensor parameters distribution.

Code Source Document Article

mrsimulator

A fast solid-state NMR spectrum simulation python library for spectral analysis.

Code Source Document

csdmpy

A python support for Core Scientific Dataset Model (CSDM).

Code Source Document Article

Publications

Statistical Learning of NMR tensors from 2D Isotropic/Anisotropic Correlation Nuclear Magnetic Resonance Spectra

Many linear inversion problems involving Fredholm integrals of the first kind are frequently encountered in the field of magnetic …

Deepansh Srivastava, Philip J. Grandinetti

Project Project DOI

Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data

The Core Scientific Dataset (CSD) model with JavaScript Object Notation (JSON) serialization is presented as a lightweight, portable, …

Deepansh Srivastava, Thomas Vosegaard, Dominique Massiot, Philip J. Grandinetti

Project DOI

Correlating structural distributions in silica glass with two-dimensional $J$-resolved spectroscopy

A two-dimensional (2D) $J$-resolved magic-angle spinning nuclear magnetic resonance (NMR) spectrum of silica glass at $^{29}$Si natural …

Deepansh Srivastava, Jay H. Baltisberger, Pierre Florian, Franck Fayon, Roman A. Shakhovoy, Michaël Deschamps, Najim Sadiki, Philip J. Grandinetti

DOI

Correlating geminal $^2J_\text{Si–O–Si}$ couplings to structure in framework silicates

The dependence of a $^{29}$Si geminal J coupling across the inter-tetrahedral linkage on local structure was examined using …

Deepansh Srivastava, Pierre Florian, Jay H. Baltisberger, Philip J. Grandinetti

DOI

Synthesis of chabazite/polymer composite membrane for $\text{CO}_2$/$\text{N}_2$ separation

There is considerable interest in using zeolite membranes for gas separations. For CO2 and N2 separation, much research has focused on …

Chenhu Sun, Deepansh Srivastava, Philip J. Grandinetti, Prabir K. Dutta

DOI

Nuances of multi-quantum excitation in solid state NMR of quadrupolar nuclei

In this article, a simple heuristic approach that is in agreement with the density operator formalism is presented for understanding …

Deepansh Srivastava, Ramesh Ramachandran

DOI

Understanding multi-quantum NMR through secular approximation

With the development of technology and improved understanding of nuclear spin–spin interactions and their behavior in …

Deepansh Srivastava, Venkata SubbaRao, R., Ramesh Ramachandran

DOI

Concept of effective Hamiltonians for transitions in multi-level systems

Employing the concept of effective Hamiltonians, an analytical theory is introduced to describe transitions in a multi-level system in …

R. Venkata SubbaRao, Deepansh Srivastava, Ramesh Ramachandran

DOI