AIMC Topic: Electrons

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Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing.

Journal of chemical information and modeling May 19, 2021
Machine learning milestones in computational chemistry are overshadowed by their unaccountability and the overwhelming zoo of tools for each specific task. A promising path to tackle these problems is using machine learning to reproduce physical magn...
Neural Networks, Computer Electrons Physical Phenomena Machine Learning Proteins
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Training artificial neural networks for precision orientation and strain mapping using 4D electron diffraction datasets.

Ultramicroscopy Mar 11, 2021
Techniques for training artificial neural networks (ANNs) and convolutional neural networks (CNNs) using simulated dynamical electron diffraction patterns are described. The premise is based on the following facts. First, given a suitable crystal str...
Electrons Neural Networks, Computer
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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 Jan 8, 2021
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...
Tin Compounds Quantum Theory Electrons Zinc Neural Networks, Computer Sulfides Copper Solar Energy
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Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge.

Journal of chemical information and modeling Dec 16, 2020
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...
Molecular Dynamics Simulation Electrons Neural Networks, Computer Machine Learning Cheminformatics
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Machine Learning Predicts Degree of Aromaticity from Structural Fingerprints.

Journal of chemical information and modeling Oct 8, 2020
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...
Machine Learning Electrons
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A machine learning framework with anatomical prior for online dose verification using positron emitters and PET in proton therapy.

Physics in medicine and biology Sep 14, 2020
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...
Proton Therapy Uncertainty Positron-Emission Tomography Radiation Dosage Signal-To-Noise Ratio Radiotherapy Dosage Image Processing, Computer-Assisted Neural Networks, Computer Electrons Machine Learning Humans
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Dose image prediction for range and width verifications from carbon ion-induced secondary electron bremsstrahlung x-rays using deep learning workflow.

Medical physics May 25, 2020
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,...
Workflow Electrons X-Rays Deep Learning Carbon
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Strain-controlled power devices as inspired by human reflex.

Nature communications Jan 16, 2020
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...
Electrochemical Techniques Electrons Transistors, Electronic Aluminum Compounds Artificial Intelligence Robotics Humans Gallium Reflex
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Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton-induced positron emitters.

Medical physics Oct 10, 2019
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 ...
Protons Machine Learning Electrons Monte Carlo Method Radiation Dosage Proton Therapy Radiotherapy Dosage
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Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs).

Physics in medicine and biology Sep 4, 2019
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...
Proton Therapy Uncertainty Feasibility Studies Electrons Positron-Emission Tomography Phantoms, Imaging Neural Networks, Computer Humans Tomography, X-Ray Computed
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