Trending Research
Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation
mzjb/deeph-pack
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The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science.
Materials Science Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics Computational Physics Quantum Physics
Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations
Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials.
Materials Science Computational Physics
Disorder in Andreev reflection of a quantum Hall edge
autonomousvision/stylegan_xl
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We find the statistical distribution of the conductance and its dependence on electron density, magnetic field, and temperature.
Mesoscale and Nanoscale Physics Superconductivity
Temperature Steerable Flows and Boltzmann Generators
noegroup/bgflow
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Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples of a physical system's equilibrium density.
Statistical Mechanics
Prediction of highly cited papers
In an article written five years ago [arXiv:0809. 0522], we described a method for predicting which scientific papers will be highly cited in the future, even if they are currently not highly cited.
Physics and Society Digital Libraries Social and Information Networks
Promoting global stability in data-driven models of quadratic nonlinear dynamics
Second, we illustrate how to modify the objective function in machine learning algorithms to promote globally stable models, with implications for the modeling of fluid and plasma flows.
Fluid Dynamics Computational Physics Plasma Physics
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
lululxvi/deepxde
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Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering.
Computational Physics
Investigating Quantum Approximate Optimization Algorithms under Bang-bang Protocols
google-research/google-research
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The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices.
Quantum Physics
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
txie-93/cgcnn
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Phys. Rev. Lett. 2017
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.
Ranked #3 on
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Formation Energy
Materials Science
Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution
Numerical weather prediction has traditionally been based on physical models of the atmosphere.
Atmospheric and Oceanic Physics
