AIMC Topic: Static Electricity

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Improvement of the Force Field for -d-Glucose with Machine Learning.

Molecules (Basel, Switzerland) Nov 5, 2021
While the construction of a dependable force field for performing classical molecular dynamics (MD) simulation is crucial for elucidating the structure and function of biomolecular systems, the attempts to do this for glycans are relatively sparse co...
Machine Learning Algorithms Models, Theoretical Molecular Dynamics Simulation Molecular Conformation Static Electricity Molecular Structure Glucose
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Machine learning differentiates enzymatic and non-enzymatic metals in proteins.

Nature communications Jun 17, 2021
Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the active sites of metalloenzymes and inactive metal binding sites, it is challenging to differentiate b...
Enzymes Metalloproteins Models, Molecular Catalysis Static Electricity Metals Machine Learning Catalytic Domain Binding Sites Databases, Protein Algorithms
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Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems.

Journal of chemical theory and computation Apr 5, 2021
Quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations have been developed to simulate molecular systems, where an explicit description of changes in the electronic structure is necessary. However, QM/MM MD simulations are ...
Molecular Dynamics Simulation Quantum Theory Static Electricity Machine Learning
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Uncertainty Quantification and Sensitivity Analysis of Partial Charges on Macroscopic Solvent Properties in Molecular Dynamics Simulations with a Machine Learning Model.

Journal of chemical information and modeling Mar 17, 2021
The molecular dynamics (MD) simulation technique is among the most broadly used computational methods to investigate atomistic phenomena in a variety of chemical and biological systems. One of the most common (and most uncertain) parametrization step...
Molecular Dynamics Simulation Solvents Static Electricity Machine Learning Uncertainty
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Electro-pneumatic pumps for soft robotics.

Science robotics Feb 17, 2021
Soft robotics has applications in myriad fields from assistive wearables to autonomous exploration. Now, the portability and the performance of many devices are limited by their associated pneumatic energy source, requiring either large, heavy pressu...
Smart Materials Equipment Design Artificial Organs Static Electricity Muscles Pressure Wearable Electronic Devices Robotics Humans
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HASEL Artificial Muscles for a New Generation of Lifelike Robots-Recent Progress and Future Opportunities.

Advanced materials (Deerfield Beach, Fla.) Nov 9, 2020
Future robots and intelligent systems will autonomously navigate in unstructured environments and closely collaborate with humans; integrated with our bodies and minds, they will allow us to surpass our physical limitations. Traditional robots are mo...
Equipment Design Muscles Biomimetics Biomimetic Materials Static Electricity Artificial Organs Humans Animals Robotics
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Modeling and Optimization of Electrostatic Film Actuators Based on the Method of Moments.

Soft robotics Oct 1, 2020
Electrostatic film actuators represent a promising new approach to drive a soft robot, but they lack a comprehensive model to link the design parameters and actuation performance, making actuator design and parameter optimization challenging. To solv...
Mechanical Phenomena Movement Equipment Design Static Electricity Robotics
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Miniaturized Circuitry for Capacitive Self-Sensing and Closed-Loop Control of Soft Electrostatic Transducers.

Soft robotics Oct 1, 2020
Soft robotics is a field of robotic system design characterized by materials and structures that exhibit large-scale deformation, high compliance, and rich multifunctionality. The incorporation of soft and deformable structures endows soft robotic sy...
Static Electricity Muscles Robotics Transducers Musculoskeletal System
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Characterizing Protein-Ligand Binding Using Atomistic Simulation and Machine Learning: Application to Drug Resistance in HIV-1 Protease.

Journal of chemical theory and computation Jan 16, 2020
Over the past several decades, atomistic simulations of biomolecules, whether carried out using molecular dynamics or Monte Carlo techniques, have provided detailed insights into their function. Comparing the results of such simulations for a few clo...
Monte Carlo Method Molecular Dynamics Simulation Humans HIV-1 Protein Binding Machine Learning HIV Protease Inhibitors Drug Resistance, Viral Hydrogen Bonding Mutation Ligands HIV Protease Static Electricity
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Steroid identification via deep learning retention time predictions and two-dimensional gas chromatography-high resolution mass spectrometry.

Journal of chromatography. A Oct 28, 2019
Untargeted steroid identification represents a great analytical challenge even when using sophisticated technology such as two-dimensional gas chromatography coupled to high resolution mass spectrometry (GC × GCHRMS) due to the chemical similarity of...
Least-Squares Analysis Deep Learning Gas Chromatography-Mass Spectrometry Static Electricity Neural Networks, Computer Support Vector Machine Steroids
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