AIMC Topic: Electrons

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Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning.

The Science of the total environment
Microplastics quantification and classification are demanding jobs to monitor microplastic pollution and evaluate the potential health risks. In this paper, microplastics from daily supplies in diverse chemical compositions and shapes are imaged by s...

Site-Specific Regulated Memristors via Electron-Beam-Induced Functionalization of HfO.

Small (Weinheim an der Bergstrasse, Germany)
Emerging nonvolatile resistive switching, also known as the memristor, works with a distinct concept that relies mainly on the change in the composition of the active materials, rather than to store the charge. Particularly for oxide-based memristors...

Artificial intelligence "sees" split electrons.

Science (New York, N.Y.)
Machine-learning creates a density functional that accounts for fractional charge and spin.

Few-fs resolution of a photoactive protein traversing a conical intersection.

Nature
The structural dynamics of a molecule are determined by the underlying potential energy landscape. Conical intersections are funnels connecting otherwise separate potential energy surfaces. Posited almost a century ago, conical intersections remain t...

Machine Learning Approach to Calculate Electronic Couplings between Quasi-diabatic Molecular Orbitals: The Case of DNA.

The journal of physical chemistry letters
Diabatization of one-electron states in flexible molecular aggregates is a great challenge due to the presence of surface crossings between molecular orbital (MO) levels and the complex interaction between MOs of neighboring molecules. In this work, ...

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...

Using Neural Network Force Fields to Ascertain the Quality of Simulations of Liquid Water.

The journal of physical chemistry. B
Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the level, inc...

Neural network representation of electronic structure from ab initio molecular dynamics.

Science bulletin
Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. We introduce a transferable high-fidelity neural network representation of such data i...

Simulation and machine learning modelling based comparative study of InAlGaN and AlGaN high electron mobility transistors for the detection of HER-2.

Analytical methods : advancing methods and applications
The detection of the cancer biomarker human epidermal growth factor receptor 2 (HER-2) has always been challenging at the early stages of cancer due to its very small presence. A systematic study of biosensors to achieve optimum sensitivity is of par...

Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy.

ACS nano
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent succe...