Trending Research
A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures
ymahlau/fdtdx
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We introduce an efficient open-source python package for the inverse design of three-dimensional photonic nanostructures using the Finite-Difference Time-Domain (FDTD) method.
Optics Computational Physics
AFLOW: An automatic framework for high-throughput materials discovery
Recent advances in computational materials science present novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds and metastable structures, electronic structure, surface, and nano-particle properties.
Materials Science
PyGRO: a Python Integrator for General Relativistic Orbits
Advancement in recent years in the field of experimental gravitation has allowed to test the equivalence principle in regimes that were previously unexplored, allowing for unprecedented verifications of general relativity and also enabling tests of alternative theories of gravity.
General Relativity and Quantum Cosmology
PtyRAD: A High-performance and Flexible Ptychographic Reconstruction Framework with Automatic Differentiation
chiahao3/ptyrad
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Electron ptychography has recently achieved unprecedented resolution, offering valuable insights across diverse material systems, including in three dimensions.
Materials Science Optics
Climate in a Bottle: Towards a Generative Foundation Model for the Kilometer-Scale Global Atmosphere
NVlabs/cBottle
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AI emulators offer a path to compressing, boosting limited ensembles, and improving the latency of interacting with petabyte-scale climate prediction data.
Atmospheric and Oceanic Physics
Seismic Full-Waveform Inversion Using Deep Learning Tools and Techniques
ar4/deepwave
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I demonstrate that the conventional seismic full-waveform inversion algorithm can be constructed as a recurrent neural network and so implemented using deep learning software such as TensorFlow.
Geophysics Computational Physics
NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting their broader use.
Materials Science
A foundation model for atomistic materials chemistry
Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales.
Chemical Physics Materials Science
Geodesic intersections
A complete treatment of the intersections of two geodesics on the surface of an ellipsoid of revolution is given.
Geophysics
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications.
Chemical Physics
