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Electrons

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Automatic recognition of ligands in electron density by machine learning.

Bioinformatics (Oxford, England)
MOTIVATION: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without so...

Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs).

Physics in medicine and biology
Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distr...

Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters.

Medical physics
PURPOSE/OBJECTIVE(S): Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution ...

Strain-controlled power devices as inspired by human reflex.

Nature communications
Bioinspired electronics are rapidly promoting advances in artificial intelligence. Emerging AI applications, e.g., autopilot and robotics, increasingly spur the development of power devices with new forms. Here, we present a strain-controlled power d...

Dose image prediction for range and width verifications from carbon ion-induced secondary electron bremsstrahlung x-rays using deep learning workflow.

Medical physics
PURPOSE: Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB x-ray images are not directly correlated to the dose images. In addition,...

A machine learning framework with anatomical prior for online dose verification using positron emitters and PET in proton therapy.

Physics in medicine and biology
We developed a machine learning framework in order to establish the correlation between dose and activity distributions in proton therapy. A recurrent neural network was used to predict dose distribution in three dimensions based on the information o...

Machine Learning Predicts Degree of Aromaticity from Structural Fingerprints.

Journal of chemical information and modeling
Prediction of whether a compound is "aromatic" is at first glance a relatively simple task-does it obey Hückel's rule (planar cyclic π-system with 4n + 2 electrons) or not? However, aromaticity is far from a binary property, and there are distinct va...

Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge.

Journal of chemical information and modeling
Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom typing or relatively expensive quantum mechanical computations to generate a density to be p...

Doping-Induced Charge Localization Suppresses Electron-Hole Recombination in Copper Zinc Tin Sulfide: Quantum Dynamics Combined with Deep Neural Networks Analysis.

The journal of physical chemistry letters
Nonradiative electron-hole recombination constitutes a major route for charge and energy losses in copper zinc tin sulfide (CZTS) solar cells. Using a combination of nonadiabatic (NA) molecular dynamics and deep neural networks (DNN), we demonstrated...

Synaptic Weight Evolution and Charge Trapping Mechanisms in a Synaptic Pass-Transistor Operation With a Direct Potential Output.

IEEE transactions on neural networks and learning systems
We present an intensive study on the weight modulation and charge trapping mechanisms of the synaptic transistor based on a pass-transistor concept for the direct voltage output. In this article, the pass-transistor concept for a metal-oxide-semicond...