Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation

mzjb/deeph-pack 8 Apr 2021

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

58
0.05 stars / hour

Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations

brucefan1983/GPUMD 15 Jun 2022

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

132
0.05 stars / hour

Disorder in Andreev reflection of a quantum Hall edge

autonomousvision/stylegan_xl 2 Jan 2022

We find the statistical distribution of the conductance and its dependence on electron density, magnetic field, and temperature.

Mesoscale and Nanoscale Physics Superconductivity

629
0.05 stars / hour

Temperature Steerable Flows and Boltzmann Generators

noegroup/bgflow 3 Aug 2021

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

63
0.05 stars / hour

Prediction of highly cited papers

UKPLab/refresh2018-predicting-trends-from-arxiv 30 Oct 2013

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

25
0.04 stars / hour

Promoting global stability in data-driven models of quadratic nonlinear dynamics

dynamicslab/pysindy 5 May 2021

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

732
0.04 stars / hour

A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

lululxvi/deepxde 10 Nov 2021

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

1,165
0.03 stars / hour

Investigating Quantum Approximate Optimization Algorithms under Bang-bang Protocols

google-research/google-research 27 May 2020

The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices.

Quantum Physics

24,611
0.03 stars / hour

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

txie-93/cgcnn 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.

Band Gap Formation Energy Materials Science

374
0.03 stars / hour

Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution

pangeo-data/WeatherBench 19 Aug 2020

Numerical weather prediction has traditionally been based on physical models of the atmosphere.

Atmospheric and Oceanic Physics

385
0.03 stars / hour