Ask Erno: a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

A multispectral camera setup is used to infer a 2D map of plasma parameters in a tokamak from spectral emissions. However, the light measured by these cameras is line integrated in the toroidal direction, whereas emissivities on the poloidal plane are nece ...

Machine learning has experienced significant growth in the smart building sector, whether for building modeling or energy management. Data-driven approaches leverage available measurements to bypass the slow and costly calibration of physics-based models, ...

Machine learning has demonstrated superior performance in predicting vast materials properties. However, predicting a spectral-like continuous material property such as phonon density of states (DOS) is more challenging for machine learning. In this work, ...

Accurately measuring behavior is critical to understanding brain function. While traditional behavioral analysis has relied on human observation and manual processing, advances in machine learning and computer vision have begun to automate the field. This ...

Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfun ...

Solid-state photochemically induced dynamic nuclear polarization (photo-CIDNP) is a nuclear magnetic resonance spectroscopy technique in which nuclear spin hyperpolarization is generated upon optical irradiation of an appropriate donor-acceptor system. Unt ...

We investigate the magnetic structure and magnetoelectric(ME) effect in the high -field phase of the antiferromagnet LiFePO 4 above the critical field of 31 T. A neutron diffraction study in pulsed magnetic fields reveals the propagation vector to be q = 0 ...

Conducting neutron scattering experiments in the presence of high pulsed magnetic fields, namely above 40 T, provides valuable information about the magnetic structures of materials. However, these experiments are challenging and time-consuming because the ...

Dynamic nuclear polarization (DNP) combined with high magnetic fields and fast magic angle spinning (MAS) has opened up a new avenue for the application of exceptionally sensitive 1H NMR detection schemes to study protonated solids. Recently, it has been s ...

129Xe dissolution dynamic nuclear polarization (DNP) is a controversial topic. The gold standard technique for hyperpolarized xenon magnetic resonance imaging (MRI) is spin exchange optical pumping, which received FDA approval in 2022. Nevertheless, the ve ...

Anomaly detection (AD) is essential in identifying rare and often critical events in complex systems, finding applications in fields such as network intrusion detection, financial fraud detection, and fault detection in infrastructure and industrial system ...

Motivation is a multifaceted phenomenon that we explore within the framework of decision-making. Through this cognitive process, actions are directed towards specific goals by performing a trade-off between the cost and benefit of an action. The dorsomedia ...

This thesis investigates the magnetic properties of single atoms and molecules adsorbed on thin magnesium oxide decoupling layers, grown on a silver single crystal. To address these systems experimentally, we use a low temperature scanning tunneling micros ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

Prediction is a vital component of motion planning for autonomous vehicles (AVs). By reasoning about the possible behavior of other target agents, the ego vehicle (EV) can navigate safely, efficiently, and politely. However, most of the existing work overl ...

In the domain of computational structural biology, predicting protein interactions based on molecular structure remains a pivotal challenge. This thesis delves into this challenge through a series of interconnected studies. The first chapter introduces th ...

Low throughput is one of dissolution Dynamic Nuclear Polarization (dDNP) main shortcomings. Especially for clinical and preclinical applications, where direct 13C nuclei polarization is usually pursued, it takes hours to generate one single hyperpolarized ...

DNA-wrapped single-walled carbon nanotubes (DNA-SWCNTs) have demonstrated great versatility as optical sensors. SWCNTs emit a near-infrared fluorescence that is responsive to the slightest changes in their environment, enabling the creation of sensors that ...

One key bottleneck of solid-state NMR spectroscopy is that H-1 NMR spectra of organic solids are often very broad due to the presence of a strong network of dipolar couplings. We have recently suggested a new approach to tackle this problem. More specifica ...

Structure determination of materials is key to understanding their physical properties. While single-crystal X-ray diffraction is the gold standard for structures displaying long-range order, many materials of interest are polycrystalline and/or disordered ...

Ask Erno: a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra

Chattez avec Graph Search

Machine learning enhanced tomographic reconstruction for multispectral imaging on TCV

Fully data-driven and modular building thermal control with physically consistent modeling

Rapid prediction of phonon density of states by crystal attention graph neural network and high-throughput screening of candidate substrates for wide bandgap electronic cooling

From Pose to Behavior: Advancing Multi-Individual Pose Estimation and Hierarchical Action Segmentation with Transformers

Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers

Magic Angle Spinning Solid-State 13C Photochemically Induced Dynamic Nuclear Polarization by a Synthetic Donor-Chromophore-Acceptor System at 9.4 T

Magnetic structure and magnetoelectric properties of the spin-flop phase in LiFePO4

High brightness neutron source optimized for very high pulsed magnetic field experiments

Optimal sensitivity for 1H detected relayed DNP of organic solids at fast MAS

129Xe Dynamic Nuclear Polarization Demystified: The Influence of the Glassing Matrix on the Radical Properties

NNG-Mix: Improving Semi-Supervised Anomaly Detection with Pseudo-Anomaly Generation

Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study

Magnetism of Single Surface Adsorbed Atoms Studied with Radio-Frequency STM

Unveiling the complexity of learning and decision-making

Dynamic Voxels Based on Ego-Conditioned Prediction: An Integrated Spatio-Temporal Framework for Motion Planning

Predicting protein interactions using geometric deep learning on protein surfaces

Multi-sample/multi-nucleus parallel polarization and monitoring enabled by a fluid path technology compatible cryogenic probe for dissolution dynamic nuclear polarization

Prediction of mycotoxin response of DNA-wrapped nanotube sensor with machine learning

Two-dimensional Pure Isotropic Proton Solid State NMR

NMR Crystallography in the Big Data Era: New Methods and Applications Powered by Machine Learning