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A novel approach to analyze particle behavior in fluids, focusing on non-spherical shapes.
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A data-driven approach to understanding lithium plating in lithium-ion batteries.
Avesta Ahmadi, Kevin J. Sanders, Gillian R. Goward
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A look into the behavior of charged particles in various states of matter.
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Researchers leverage quantum computing to study electron interactions in materials.
Adam Prokofiew, Nidhish Sharma, Steven Schnetzer
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Researchers are improving molecular modeling accuracy with machine learning techniques.
Gustavo R. Pérez-Lemus, Yinan Xu, Yezhi Jin
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NMRNet enhances accuracy in predicting chemical shifts using deep learning techniques.
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This study examines Chua's and Lorentz circuits using machine learning for understanding neuronal behavior.
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SOLAX aids in the simulation of complex quantum systems for researchers.
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New method enhances analysis of complex systems through Symplectic Graph Neural Networks.
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A novel approach to handle dynamic boundary conditions in wave equations.
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A new method enhances neural network performance for solving complex physics equations.
Tianchi Yu, Yiming Qi, Ivan Oseledets
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Advanced Python code facilitates the study of transversity PDFs in particle physics.
Congzhou M Sha, Bailing Ma
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New methods combine simple models with advanced grids for efficient subsurface analysis.
Wouter Deleersnyder, Evert Slob
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Machine learning is transforming how we study hadronization in high-energy physics.
Gábor Bíró, Gábor Papp, Gergely Gábor Barnaföldi
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New methods enhance understanding of phonon interactions with lattice defects in materials.
Zhun-Yong Ong
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This study focuses on how particles react on surfaces and the factors involved.
Denis S. Grebenkov
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Introducing an efficient method for studying electron behavior in low-temperature plasma.
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Machine learning enhances the prediction of thermal conductivity in materials, saving time and resources.
Yagyank Srivastava, Ankit Jain
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New DFXM model reveals dislocation structures and their impact on material behavior.
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A new package simplifies data analysis for researchers in laser spectroscopy.
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Researchers utilize M3GNet for efficient predictions in material behavior.
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A computer model improves the study of schreibersite, a mineral linked to early life.
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A method to study how events cluster over time.
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Exploring the fusion of machine learning and molecular mechanics for better simulations.
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Researchers use machine learning to predict information spread in quantum systems.
John Tanner, Jason Pye, Jingbo Wang
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This study presents a model for understanding cell segregation based on contraction dynamics.
Emanuel F. Teixeira, Carine P. Beatrici, Heitor C. M. Fernandes
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Investigating stability in neutron and boson stars through configurational entropy analysis.
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Researchers develop a new technique to simulate interactions of quantum emitters in complex arrangements.
Raphael Holzinger, Oriol Rubies-Bigorda, Susanne F. Yelin
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New methods improve data analysis in superconductivity research.
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Exploring chimera states and their significance in nature and technology.
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Learn how Langevin dynamics improves parameter estimation over traditional methods.
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MC/DC software enhances neutron transport simulations for better accuracy and efficiency.
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A look into how curvature is calculated in discrete gravity and its applications.
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New insights into current flow in vacuum diodes enhance electronic device design.
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Examining how environmental arrangements impact quantum system simulations.
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Recent progress in recurrence analysis enhances our understanding of system behavior over time.
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A simplified approach to compute Wannier functions improves material research.
Abinand Gopal, Hanwen Zhang
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Discover how FastVPINNs improve fluid dynamics modeling using neural networks.
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New methods utilize single snapshots for effective turbulence analysis.
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Exploring the Lattice Boltzmann Method for fluid dynamics in transonic flows.
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