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Register a new userNeural networks in double electron-electron resonance: a practical guide
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This is a methodological guide to the use of deep neural networks in the processing of double electron-electron resonance (DEER, aka PELDOR) data encountered in structural biology, organic photovoltaics, photosynthesis research, and other domains featuring long-lived radical pairs. DEER spectroscopy uses distance dependence of magnetic dipolar interactions; measuring a single well-defined distance is straightforward, but extracting distance distributions is a hard and mathematically ill-posed problem requiring careful regularisation and background fitting. Neural networks do this exceptionally well, but their robust black box reputation hides the complexity of their design and training - particularly when the training dataset is effectively infinite. The objective of this paper is to give insight into training against infinite databases, to shed some light on the processes inside the neural net, and to provide a practical data processing flowchart for structural biology work.
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When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In response, we propose to improve the librarys coverage by augmenting it with synthetic spectra that are predicted using machine learning. We contribute a lightweight neural network model that quickly predicts mass spectra for small molecules. Achieving high accuracy predictions requires a novel neural network architecture that is designed to capture typical fragmentation patterns from electron ionization. We analyze the effects of our modeling innovations on library matching performance and compare our models to prior machine learning-based work on spectrum prediction.
Shape resonances in physics and chemistry arise from the spatial confinement of a particle by a potential barrier. In molecular photoionization, these barriers prevent the electron from escaping instantaneously, so that nuclei may move and modify the potential, thereby affecting the ionization process. By using an attosecond two-color interferometric approach in combination with high spectral resolution, we have captured the changes induced by the nuclear motion on the centrifugal barrier that sustains the well-known shape resonance in valence-ionized N$_2$. We show that despite the nuclear motion altering the bond length by only $2%$, which leads to tiny changes in the potential barrier, the corresponding change in the ionization time can be as large as $200$ attoseconds. This result poses limits to the concept of instantaneous electronic transitions in molecules, which is at the basis of the Franck-Condon principle of molecular spectroscopy.
The SMEFTsim package is designed to enable automated computations in the Standard Model Effective Field Theory (SMEFT), where the SM Lagrangian is extended with a complete basis of dimension six operators. It contains a set of models written in FeynRules and pre-exported to the UFO format, for usage within Monte Carlo event generators. The models differ in the flavor assumptions and in the input parameters chosen for the electroweak sector. The present document provides a self-contained, pedagogical reference that collects all the theoretical and technical aspects relevant to the use of SMEFTsim and it documents the release of version 3.0. Compared to the previous release, the description of Higgs production via gluon-fusion in the SM has been significantly improved, two flavor assumptions for studies in the top quark sector have been added, and a new feature has been implemented, that allows the treatment of linearized SMEFT corrections to the propagators of unstable particles.
Magnetic impurities in diamond influence the relaxation properties and thus limit the sensitivity of magnetic, electric, strain, and temperature sensors based on nitrogen-vacancy color centers. Diamond samples may exhibit significant spatial variations in the impurity concentrations hindering the quantitative analysis of relaxation pathways. Here, we present a local measurement technique which can be used to determine the concentration of various species of defects by utilizing double electron-electron resonance. This method will help to improve the understanding of the physics underlying spin relaxation and guide the development of diamond samples, as well as offering protocols for optimized sensing.
We report the nanoscale spin detection and electron paramagnetic resonance (EPR) spectrum of copper (Cu$^{2+}$) ions via double electron-electron resonance with single spins in diamond at room temperature and low magnetic fields. We measure unexpectedly narrow EPR resonances with linewidths $sim 2-3$ MHz from copper-chloride molecules dissolved in poly-lysine. We also observe coherent Rabi oscillations and hyperfine splitting from single Cu$^{2+}$ ions, which could be used for dynamic nuclear spin polarization and higher sensitivity of spin detection. We interpret and analyze these observations using both spin hamiltonian modeling of the copper-chloride molecules and numerical simulations of the predicted DEER response, and obtain a sensing volume $sim (250 text{nm})^3$. This work will open the door for copper-labeled EPR measurements under ambient conditions in bio-molecules and nano-materials.
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