Magnetic Resonance Spectroscopy

Latest papers

13 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch
Oct 12, 2026Alec H. FollmerMagnetic Resonance Spectroscopy Differentiable Physics
Department of Chemistry, University of California, Davis, Davis, California 95616, United States · Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra
Aug 12, 2026Bingsen Xue, Zhuojun Jiang, Jianhao Zhang +10Magnetic Resonance Spectroscopy
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · CAS Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China · Department of Neurology, National Centre for Neurological Disorders, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai 200240, China +6
Exploiting chemical shift variability enables recovery of overlapping metabolites from 1H nuclear magnetic resonance spectra
Aug 6, 2026Jesper Løve Hinrich, Pia Susan Mayer, Bekzod Khakimov +2Non-Negative Matrix Factorization Magnetic Resonance Spectroscopy
Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads 321, Kgs. Lyngby, 2800, Denmark. · Department of Food Science, University of Copenhagen, Rolighedsvej 26, Frederiksberg, 1958, Denmark.
Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data
Jul 22, 2026Chengchun Liu, Zhiyuan Yan, Li Yuan +5Molecular Generation Magnetic Resonance Spectroscopy
School of AI for Science, Peking University Shenzhen Graduate School, Shenzhen 518055, China · State Key Laboratory of Advanced Waterproof Materials, School of Materials Science and Engineering, Peking University, Beijing 100871, China · School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen 518055, China +6
Inverse-IMPRESSION: A Graph-based Platform for Molecular Structure Elucidation from Experimental NMR Spectroscopic Properties
Jul 10, 2026Zheqi Jin, Grace Armitage, Richard Cox +3Graph Representation Learning Magnetic Resonance Spectroscopy
School of Chemistry, University of Bristol, Cantock’s Close, Bristol, BS8 1TS, U.K. · School of Engineering Mathematics and Technology, University of Bristol, Ada Lovelace Building, Tankard's Cl, Bristol BS8 1TW
A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis
Jun 18, 2026Chen Yang, Zheng Fang, Hanyu Sun +7Synthetic Data Pretraining Synthetic-to-Real Domain Adaptation
The Hong Kong University of Science and Technology (Guangzhou) · Peking Union Medical College and Chinese Academy of Medical Sciences · Xiamen University +3
Physical probes expose and alleviate chemical-environment collapse in molecular representations
May 11, 2026Jiebin Fang, Zidi Yan, Churu Mao +7Contrastive Learning Cross-Modal Representation Learning
Hainan Institute, Zhejiang University, Sanya 572025, China · Institute of Marine Biology and Pharmacology, Ocean College, Zhejiang University, Zhoushan 316021, China · School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan 316021, China +2
HyperFitS -- Hypernetwork Fitting Spectra for metabolic quantification of ${}^1$ H MR spectroscopic imaging
Apr 3, 2026Paul J. Weiser, Gulnur Ungan, Amirmohammad Shamaei +5Magnetic Resonance Spectroscopy Magnetic Resonance Imaging
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA · Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA · Computational Imaging Research Lab – Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria +4
Spectral Coherence Index: A Model-Free Metric for Protein Structural Ensemble Quality Assessment
Mar 26, 2026Yuda Bi, Huaiwen Zhang, Jingnan Sun +1Magnetic Resonance Spectroscopy
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303 USA · School of Computer Science and Technology, Xidian University, Xi’an 710126, China · Departments of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287 USA +1
Agentic LLM Workflow for MR Spectroscopy Volume-of-Interest Placements in Brain Tumors
Mar 9, 2026Sangyoon Lee, Francesca Branzoli, Małgorzata Marjańska +1Magnetic Resonance Spectroscopy Brain MRI
Center for Magnetic Resonance Research, University of Minnesota · ICM – Paris Brain Institute
Data-driven Synthesis of Magnetic Resonance Spectroscopy Data using a Variational Autoencoder
Feb 28, 2026Dennis M. J. van de Sande, Julian P. Merkofer, Sina Amirrajab +4Magnetic Resonance Spectroscopy Synthetic Data Generation
Department of Biomedical Engineering Eindhoven University of Technology Eindhoven, The Netherlands · Department of Electrical Engineering Eindhoven University of Technology Eindhoven, The Netherlands · Department of Radiology & Nuclear Medicine Maastricht University Medical Center Maastricht, The Netherlands
The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA
Feb 23, 2026Zien Ma, S. M. Shermer, Oktay Karakuş +1Magnetic Resonance Spectroscopy Sim-to-Real Transfer
School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 4AG, United Kingdom · Faculty of Science and Engineering, Swansea University, Swansea, SA2 8PP, United Kingdom
Physics-Informed Sylvester Normalizing Flows for Bayesian Inference in Magnetic Resonance Spectroscopy
May 6, 2025Julian P. Merkofer, Dennis M. J. van de Sande, Alex A. Bhogal +1Normalizing Flows Bayesian Inference
Eindhoven University of Technology, Eindhoven, The Netherlands · University Medical Center Utrecht, Utrecht, The Netherlands

Oct 12, 2026J/K move · Enter open · S save

TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch

Alec H. Follmer

Department of Chemistry, University of California, Davis, Davis, California 95616, United States · Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States

Magnetic Resonance Spectroscopy Differentiable Physics Parameter Estimation Physics-Informed ML

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Interpreting electron paramagnetic resonance (EPR) spectra commonly requires repeated forward simulation and optimization of spin Hamiltonian parameters. Established software provides mature simulation and fitting methods, but these calculations are not generally available as differentiable components within machine-learning workflows. We present TorchSpin, an open-source PyTorch framework that ports the organization and numerical conventions of EasySpin into a tensor-based computational environment. Representative calculations spanning continuous-wave EPR, ENDOR, pulse EPR, spin dynamics, trajectory simulation, and magnetometry closely reproduced corresponding EasySpin results. In matched benchmarks, TorchSpin CPU execution was faster than EasySpin for six of nine workloads although performance depended strongly on the calculation. We therefore focused on the capabilities enabled by automatic differentiation in PyTorch. In a Cu(II) powder-spectrum demonstration, feedback propagated through TorchSpin improved the quality of a supervised convolutional neural network (CNN) without using parameter labels during refinement. Models trained from randomly initialized CNNs using only noisy spectra and TorchSpin reconstruction loss achieved median similarities of

\geq

0.99. CNNs trained on TorchSpin-generated libraries also transferred without adaptation to independently generated EasySpin spectra with performance comparable to EasySpin-trained controls. Once trained, the CNN produced initial parameter estimates in approximately 1 ms per spectrum while a batch of 512 was processed in approximately 1.3 ms with GPU acceleration. TorchSpin therefore provides both a tested Python implementation of established EPR calculations and a differentiable physical layer for rapid learned inference, spectral refinement, and parameter-label-free training.