AIMC Topic: Static Electricity

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Poisson-Boltzmann-based machine learning model for electrostatic analysis.

Biophysical journal Feb 15, 2024
Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation ...
Machine Learning Static Electricity Thermodynamics Poisson Distribution Molecular Dynamics Simulation
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Bound ion effects: Using machine learning method to study the kinesin Ncd's binding with microtubule.

Biophysical journal Dec 30, 2023
Drosophila Ncd proteins are motor proteins that play important roles in spindle organization. Ncd and the tubulin dimer are highly charged. Thus, it is crucial to investigate Ncd-tubulin dimer interactions in the presence of ions, especially ions tha...
Machine Learning Animals Kinesins Tubulin Protein Multimerization Solvents Microtubules Drosophila Proteins Protein Binding Static Electricity Molecular Dynamics Simulation Ions
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A mechanics-based approach to realize high-force capacity electroadhesives for robots.

Science robotics Nov 30, 2022
Materials with electroprogrammable stiffness and adhesion can enhance the performance of robotic systems, but achieving large changes in stiffness and adhesive forces in real time is an ongoing challenge. Electroadhesive clutches can rapidly adhere h...
Static Electricity Bone Plates Fingers Humans Fractures, Bone Robotics
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Electrostatic Adhesion Clutch with Superhigh Force Density Achieved by MXene-Poly(Vinylidene Fluoride-Trifluoroethylene-Chlorotrifluoroethylene) Composites.

Soft robotics Nov 1, 2022
Electrostatic adhesion (EA) clutches are widely applied in robots, wearable devices, and virtual reality, due to their compliance, lightweight, ultrathin profile, and low power consumption. Higher force density has been constantly perpetuated in the ...
Robotics Static Electricity
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A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data.

Journal of chemical information and modeling Sep 13, 2022
Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consumin...
Static Electricity Neural Networks, Computer Iodides Microscopy, Atomic Force Calibration
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Modified Electrostatic Complementary Score Function and Its Application Boundary Exploration in Drug Design.

Journal of chemical information and modeling Sep 7, 2022
In recent years, machine learning (ML) models have been found to quickly predict various molecular properties with accuracy comparable to high-level quantum chemistry methods. One such example is the calculation of electrostatic potential (ESP). Diff...
Static Electricity Drug Design Ligands Machine Learning
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Application of Quantum Chemical Topology Force Field FFLUX to Condensed Matter Simulations: Liquid Water.

Journal of chemical theory and computation Aug 8, 2022
We present here the first application of the quantum chemical topology force field FFLUX to condensed matter simulations. FFLUX offers many-body potential energy surfaces learnt exclusively from data using Gaussian process regression. FFLUX also inc...
Machine Learning Static Electricity Water Temperature
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A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven p Predictions in Proteins.

Journal of chemical theory and computation Jul 15, 2022
Existing computational methods for estimating p values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined p shifts to train deep learning models, which are sh...
Molecular Dynamics Simulation Static Electricity Deep Learning Molecular Docking Simulation Proteins
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Self-consistent determination of long-range electrostatics in neural network potentials.

Nature communications Mar 23, 2022
Machine learning has the potential to revolutionize the field of molecular simulation through the development of efficient and accurate models of interatomic interactions. Neural networks can model interactions with the accuracy of quantum mechanics-...
Neural Networks, Computer Static Electricity Physics Machine Learning Computer Simulation
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Machine Learning-Assisted Pattern Recognition of Amyloid Beta Aggregates with Fluorescent Conjugated Polymers and Graphite Oxide Electrostatic Complexes.

Analytical chemistry Jan 27, 2022
Five fluorescent positively charged poly(-aryleneethynylene) (-) were designed to construct electrostatic complexes - with negatively charged graphene oxide (). The fluorescence of conjugated polymers was quenched by the quencher . Three electrostati...
Static Electricity Chemistry, Clinical Alzheimer Disease Machine Learning Amyloid beta-Peptides Humans Oxides Polymers Graphite
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