AIMC Topic: Static Electricity

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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...
Robotics Fingers Humans Fractures, Bone Static Electricity Bone Plates
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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...
Neural Networks, Computer Microscopy, Atomic Force Static Electricity Iodides 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...
Machine Learning Ligands Drug Design Static Electricity
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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 Temperature Water Static Electricity
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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 Molecular Docking Simulation Deep Learning 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-...
Physics Neural Networks, Computer Computer Simulation Machine Learning Static Electricity
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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...
Humans Machine Learning Graphite Polymers Static Electricity Chemistry, Clinical Oxides Alzheimer Disease Amyloid beta-Peptides
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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...
Algorithms Models, Theoretical Molecular Conformation Machine Learning Glucose Molecular Structure Static Electricity Molecular Dynamics Simulation
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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...
Binding Sites Enzymes Static Electricity Metalloproteins Catalysis Databases, Protein Algorithms Machine Learning Catalytic Domain Models, Molecular Metals
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