Data and machine learning are transforming how scientists find new materials.
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New methods improve efficiency and accuracy in electronic structure modeling.
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New methods improve light manipulation in nanostructures for better energy efficiency.
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A look into SrCuTeWO and its unique nonmagnetic properties.
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New method improves speed and accuracy in gas flow simulations.
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New techniques improve eigenvalue calculations for complex matrices efficiently.
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Investigating methods for applying boundary conditions in the Schrödinger equation simulations.
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This study examines how fast and slow active particles interact in mixtures.
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Using GNNs to predict optical properties of materials improves device design.
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A fresh approach to understanding the origins of gamma-ray bursts using machine learning.
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A simplified approach to identify crystal structures using particle coordinates.
― 4 min read
This study models straight and circular swimmers in fluid dynamics.
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Exploring the role of tensor networks in enhancing quantum machine learning.
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A new method improves efficiency in computing eigenvalues for nuclear physics.
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New method enhances air quality predictions using machine learning techniques.
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This article explores methods for modeling interactions of charged particles in plasmas.
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Study reveals decision-making in tiny organisms for efficient swimming.
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New method improves efficiency in electron simulations using Fourier neural operators.
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A new method improves the design process for compact light-control devices.
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A look at the behavior of electromagnetic waves in unique materials.
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Soot impacts climate through its unique behavior in the atmosphere.
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Research focuses on enhancing NCM materials in lithium-ion batteries through advanced doping techniques.
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New method improves accuracy in simulating plasma and neutral particle interactions.
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Exploring the effects of disorder on vortex matter in superconductors.
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Hexagonal boron nitride is vital for the future of quantum technologies.
― 4 min read
Exploring AI's role in predicting protein structures through copolymer sequences.
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This research examines how triangularity influences turbulence in tokamak plasma.
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Exploring the mechanics of graphite-moderated nuclear reactors using gaseous fuel.
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A new method enhances uncertainty measures in machine learning interatomic potentials.
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Research evaluates two codes for modeling plasma behavior in tokamaks.
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A new approach enhances accuracy in modeling tectonic movements and geological processes.
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A new method for estimating energy barriers in transitions between stable states.
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Active solids change shape and movement, impacting diverse fields like robotics and medicine.
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Machine learning techniques enhance current density reconstruction from magnetic fields.
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ELEQTRONeX enhances understanding of non-equilibrium transport in nanomaterials.
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Researchers simulate tiny molecular structures acting as mechanical switches for advanced applications.
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This method simplifies how scientists calculate partition functions using tensor networks.
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This article explores how helical and non-helical dynamos generate magnetic fields.
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New methods enhance understanding of electronic properties in materials.
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Research highlights improvements in lithium argyrodite for safer, more efficient batteries.
― 5 min read