Global NMR Discussion Meetings

Theoretical Aspects of NMR

  • Pulsar Studio: A Graphical Interface for Quantum Optimal-Control Pulse Design

    David Joseph (Germany)

    LinkedIn: David Joseph; X: @DaJo_1729; Bluesky: @dajo-1729.bsky.social

    Abstract: Quantum optimal-control methods routinely outperform analytic shaped pulses for broadband and band-selective spin manipulation and coherence transfer, and can be made robust to instrumental limitations, B_0/B_1 inhomogeneity, and relaxation. Yet their adoption remains limited by a steep tooling barrier, i.e. designing pulse shapes requires installing a scientific programming environment, learning a package API, and writing scripts by hand. We present Pulsar Studio, a cross-platform desktop application that removes this barrier by wrapping the Pulsar.jl optimal-control library behind a guided graphical interface. Crucially, end users need no programming environment and write no code. The application bundles its own Julia runtime and optimization backend, so spectroscopists install a single package and begin designing pulses immediately. During optimization, the interface streams live diagnostics — fidelity convergence, gradient norm, and an updating waveform snapshot — allowing users to judge and cancel runs interactively. Designed pulses can be exported directly to the instrument-ready formats supported by Pulsar.jl. The complete experiment — system definition, constraints, and run history — are saved as portable project files, supporting reproducible and shareable workflows. By making quantum optimal-control pulse design approachable without any coding, Pulsar Studio lowers the barrier for adapting robust pulse engineering into everyday magnetic-resonance practice.

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  • Pulsar.jl: A Unified Julia Framework for Pulse Design Across Magnetic Resonance and Quantum Technologies

    David Joseph (Germany)

    LinkedIn: David Joseph; X: @DaJo_1729; Bluesky: @dajo-1729.bsky.social

    Abstract: Pulse shape design underpins modern magnetic resonance and, increasingly, other quantum technologies — yet its toolkits remain fragmented across sub-fields. We present Pulsar.jl (Pulse Design Library for Spin Control Algorithms and Rollout), an open-source Julia package that unifies pulse optimization across magnetic resonance (solution-state NMR, MAS solid-state NMR, EPR, MRI, and DNP) and quantum technology/computing platforms (transmon, trapped-ion, neutral-atom, and NV-center). A shared, layered core enables techniques to cross-pollinate between communities and lets users extend the framework to any application requiring pulse shaping. Pulsar.jl supports closed- and open-system (Lindblad) dynamics, automatic differentiation, and CPU/CUDA/Metal acceleration. Its algorithm layer comprises over 40 optimization methods, spanning quantum optimal control (GRAPE, Krotov, GOAT, CRAB, L-BFGS versions etc.) and metaheuristics (CMA-ES, particle swarm, basin hopping, Annealing, Monte Carlo etc.) and more. Optimized pulses can be exported directly to instrument-ready formats for magnetic resonance (Bruker, JEOL, EPR), quantum computing (Qiskit, Quil-T, QUA), and MRI. A driver-based benchmarking framework further enables matched, canonically re-evaluated comparisons against established packages including Spinach, SIMPSON, QuTiP, Krotov.jl, and Quandary, helping users identify the best tool for their application. By consolidating multi-regime physics, a comprehensive algorithm library, and reproducible cross-solver benchmarking, Pulsar.jl bridges pulse design for magnetic resonance and quantum technologies in a single framework.
    Github: https://github.com/DaJo2025/Pulsar.jl and
    documentation: https://dajo2025.github.io/Pulsar.jl/stable/

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  • Frequency-dependent NMR relaxation : Insights into the Structure and Dynamics of ‘WiS’ Electrolytes

    Angel Mary Chiramel Tony (University of Rostock, Germany)

    LinkedIn: @Angel Mary Chiramel Tony; X: @AngelMaryCT1; Bluesky: @angeltony2000.bsky.social

    Abstract: “The “Water-in-Salt Electrolytes” are promising candidates for safer high-voltage aqueous lithium-ion chemistry. Our system of concern for the NMR relaxation investigation is Li-bis(trifluoromethanesulfonyl)imide with H2O/D2O mixtures in 1:3, 1:4 and 1:5 ratios for shedding light on the structure and dynamics of this battery electrolyte as a model system. The frequency-dependant R1(spin-lattice) and R2(spin-spin) relaxation rates for several orders of magnitude is calculated with the computational framework employing MD simulations and applying a correction factor to account for the system size dependency and accessible time scales. Our approach is based on combining the analytical theory of Hwang and Freed (HF) for the long-range intermolecular contribution of the magnetic dipole-dipole correlation function with MD simulations . We show that the correlation functions due to the HF-theory do asymptotically converge with our MD simulation results at long times. We are successful in dissecting the intermolecular and intramolecular contribution of relaxation rates describing the translational and rotational dynamics with the NMR active nuclei 1H and 19F on water and anion molecules respectively. The results show that both longitudinal and transverse relaxation rates increase with increasing temperature and water content indicating enhanced dynamics. Morever the complimentery calculations shed light into the Li ion transport mechanism in the system. The key finding is that Li ion dynamics is compensated with anion repalcing water in highly concentrated systems making it an ideal candidate for battery electrolytes.”

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  • Physics-Informed Neural Network for Conformational State Prediction from NMR Spectra

    David Joseph (Germany)

    LinkedIn: David Joseph; X: @DaJo_1729; Bluesky: @dajo-1729.bsky.social

    Abstract: Determining conformational ensembles—and thereby the underlying conformational landscape—of peptides and proteins remains a central challenge in structural biology. Conventional NMR approaches rely on iterative, expert-driven interpretation of chemical shifts, NOEs, and scalar couplings, which can be time-consuming and difficult to scale. We introduce a physics-informed neural network for conformational state prediction directly from NMR spectra. The proposed model is a transformer-based architecture that maps raw 2D NMR experiments—including NOESY, HSQC, and TOCSY spectra—to multi-state conformational ensembles. For each state, the network predicts backbone torsion angles, side-chain dihedrals, inter-residue distances, and corresponding Boltzmann populations. Training is performed on a fully synthetic dataset generated by combining physics-based conformational sampling with a differentiable NMR pulse-sequence simulator, enabling the creation of paired (spectrum, ensemble) data without reliance on experimentally determined structures. Preliminary results on held-out validation data indicate that the model accurately recovers backbone geometries and population distributions from spectral inputs alone. Ongoing work focuses on extending the approach to longer sequences, variable ensemble sizes, and application to experimental NMR datasets.

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  • Evaluation of an 80 MHz Benchtop NMR System for Spin Lattice Relaxation (T₁) Measurements in Coffee Extracts

    Mailinda Ayu Hana Margareta (Universitas Negeri Malang, Indonesia)

    Abstract: The increasing demand for rapid and cost-effective analytical methods for coffee quality assessment has highlighted the need for accessible techniques capable of probing molecular behavior in complex coffee matrices. Although high field Nuclear Magnetic Resonance (NMR) spectroscopy is the standard approach for spin lattice relaxation (T₁) measurements, its widespread use remains limited by high acquisition costs and specialized infrastructure requirements. This study evaluates the capability of an 80 MHz Benchtop NMR system to perform T₁ measurements directly in coffee extracts. Building upon an established relaxation delay (d1) optimization protocol, inversion recovery experiments were conducted, and relaxation curves were analyzed using nonlinear fitting to determine T₁ values for resolved proton resonances in the coffee extracts. The results demonstrate that the Benchtop NMR system successfully generated reliable relaxation curves and accurately determined distinct T₁ values for multiple proton resonances within the complex coffee matrix. The reproducibility of the fitting results indicates that the 80 MHz Benchtop NMR possesses sufficient sensitivity and stability for molecular relaxation studies despite its relatively low magnetic field strength. These findings demonstrate the feasibility of employing Benchtop NMR as an accessible analytical platform for routine T₁ measurements in coffee extracts and support its potential application in molecular characterization and quality assessment within the coffee industry.

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  • Improved 2D-HMQC Spectroscopy Through Perfect Echo Refocusing and ASAP Polarization Transfer

    Nidhi Tiwari (Centre of Biomedical Research, India)

    LinkedIn: Nidhi Tiwari, X: @TiwariNidhi05

    Abstract: The speed of multidimensional NMR spectroscopy can be increased by an order of magnitude by shortening the recycle delay between scans. The consequent loss of longitudinal magnetization due to incomplete relaxation can be retrieved if undisturbed polarization is transferred from nearby proton spins not directly attached to 13C. In ASAP (Acceleration by Sharing Adjacent Polarization) HMQC, an ASAP block based on homonuclear Hartmann-Hahn mixing is incorporated, which consistently provides higher signal enhancement under identical total preparation time by transferring polarization from 12C attached (donor) protons to 13C attached (acceptor) protons, leading to repeated revival of detectable magnetization during short recovery delays.[1] Later on, this ASAP mechanism was also demonstrated to be useful for HSQC and NOAH (NMR by Ordered Acquisition using 1H detection); however, in HSQC, further improvement was achieved using the ZIP element, which overcomes the JHH (homonuclear ¹H-¹H J-evolution) modulation of the remote proton transverse magnetization by storing it before the start of the t1 evolution.
    Recently, we have been addressing this JHH modulation in the HMQC class of experiments by combining the concept of ASAP and Perfect Echo-based refocusing of JHH in HMQC. This Perfect Echo-based ASAP-HMQC offers performance similar to that of ASAP-HSQC and better than that of only ASAP-HMQC.
    Comparative analysis of ASAP-HMQC, ASAP-HSQC, and Perfect Echo ASAP-HMQC will be presented in the work.

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  • Isolation of Megastigmanes from Ficus sycomorus and In Silico Design of Novel Cyclohexenone Derivatives as Tubulin Inhibitors for Breast Cancer Therapy

    Dauda Garba (University of Abuja Nigeria, Nigeria)

    LinkedIn: Dauda Garba

    Abstract: Breast cancer demands novel therapies with improved efficacy and reduced toxicity. Tubulin, particularly the colchicine-binding site, is an ideal target for disrupting microtubule dynamics. While medicinal plants offer chiral bioactive compounds, determining their absolute configuration is challenging. This study integrates phytochemistry and computational design to develop cyclohexenone-based tubulin inhibitors. Two megastigmane derivatives, vomifoliol (A1) and its 13-hydroxy analog (A2), were isolated from Ficus sycomorus and characterized via NMR, LCMS, and ECD. Guided by their scaffold and SAR, fourteen derivatives were designed in silico. SwissADME and ProTox-III confirmed drug-likeness and favorable ADMET profiles. Molecular docking against tubulin (PDB: 1SA0) identified six compounds with superior binding (−8.0 to −9.0 kcal/mol) over colchicine (−7.9 kcal/mol). Lig9 showed the strongest affinity (−9.0 kcal/mol), with key interactions at CYS241, LEU242, and ILE378. These results position Lig9 as a promising lead for breast cancer therapy, warranting experimental validation.

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  • Single-Scan Characterization of 14N Nuclei via 1H-Detected Rotating-Frame Relaxometry

    Florin Teleanu (New York University, United States)

    LinkedIn: Florin Teleanu, X: @teleanuflorin, BlueSky: @teleanuflorin.bsky.social

    Abstract: 14N NMR is notoriously difficult to perform in liquids due to the very fast spin relaxation and the large quadrupolar couplings, which render many signals invisible. We show here how 14N nuclei of biomolecular constituents can be probed indirectly by reintroducing the scalar relaxation of the second kind contribution to the polarization lifetimes of J-coupled protons in double resonance spin-locking experiments. The enhanced 1H relaxation rates in the rotating-frame allow for direct evaluation of nitrogen chemical shift and polarization lifetimes, from which one- and even two-bond 1H-14N scalar couplings as well as 14N quadrupolar interactions can be determined. We demonstrate the versatility of this method by characterizing 1H-14N spin pairs in several molecules of biological importance, showing proton relaxation enhancements beyond one order of magnitude. We further observe a pronounced effect from intermolecular hydrogen bonding. Our approach can be readily integrated into existing biomolecular NMR methodologies, as demonstrated here for 1H-detected relaxation-editing experiments with water suppression. This method provides access to nitrogen’s picosecond-modulated quadrupolar interaction via single-scan proton detection in systems that would otherwise yield almost no detectable direct 14N signal even after averaging over thousands of transients.

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  • Prediction of 13C-NMR Chemical Shifts of Small Organic and Drug Molecules Using Machine Learning

    Surajit Das (Tata Institute of Fundamental Research Hyderabad, India)

    LinkedIn: Surajit Das, X: surajit_das_x, BlueSky: surajitdas.bsky.social

    Abstract: Accurate prediction of NMR chemical shifts is essential for mapping the structure-property relationships of complex molecules. In this work, we use the kernel ridge regression (KRR) machine learning architecture with an in-house-built aBoB-RBF(n) atomic representation to predict 13C-NMR chemical shifts. To train the model, we use the QM9NMR dataset, which contains 130,831 molecules with up to 9 non-hydrogen (CONF) atoms, encompassing nearly 832k carbon atomic environments. This vast pool of chemical diversity allows us to train on 100k data points with a 50k test set, yielding a mean absolute error of 1.69 ppm, an improvement over previously reported models. We also demonstrate model performance on diverse validation sets, including the drug-like Drug12 and Drug40 datasets; the GDBm dataset, which contains an increasing number of CONF atoms; and the Pyrimidinone dataset, which features biologically relevant substituted uracil and pyrimidinone molecules. Additionally, we developed a Python-based mlqm9nmr module, available on GitHub, that allows users to quickly predict chemical shifts using our KRR model. Finally, we developed a web-based interface that uses a given molecule’s SMILES string to predict 13C NMR chemical shifts with the trained KRR model.

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