Crystal Structure Generation with Autoregressive Large Language Modeling

lantunes/crystallm 10 Jul 2023

Quickly generating and predicting inorganic crystal structures is important for the discovery of new materials, which can target applications such as energy or electronic devices.

Materials Science

0.03 stars / hour

Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities

lbnlp/matbert 21 Apr 2022

Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles.

Materials Science

0.03 stars / hour

The geometry of nonlinear least squares with applications to sloppy models and optimization

JuliaOpt/LsqFit.jl 7 Oct 2010

Parameter estimation by nonlinear least squares minimization is a common problem with an elegant geometric interpretation: the possible parameter values of a model induce a manifold in the space of data predictions.

Statistical Mechanics Computational Physics Data Analysis, Statistics and Probability

0.03 stars / hour

Data assimilation for chaotic dynamics

nansencenter/DAPPER 10 Oct 2020

This chapter reviews recent findings from investigations on the impact of chaos on data assimilation methods: for the Kalman filter and smoother in linear systems, analytic results are derived; for their ensemble-based versions and nonlinear dynamics, numerical results provide insights.

Data Analysis, Statistics and Probability Chaotic Dynamics

0.03 stars / hour

Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling

materialsvirtuallab/maml 24 Jul 2023

In this work, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space.

Materials Science

0.03 stars / hour

Quantum Error Mitigation

unitaryfund/mitiq 3 Oct 2022

For quantum computers to successfully solve real-world problems, it is necessary to tackle the challenge of noise: the errors which occur in elementary physical components due to unwanted or imperfect interactions.

Quantum Physics

0.03 stars / hour

On-the-fly algorithm for Dynamic Mode Decomposition using Incremental Singular Value Decomposition and Total Least Squares

pydmd/pydmd 31 Mar 2017

Dynamic Mode Decomposition (DMD) is a useful tool to effectively extract the dominant dynamic flow structure from a unsteady flow field.

Fluid Dynamics

0.03 stars / hour

Climate Modelling in Low-Precision: Effects of both Deterministic & Stochastic Rounding

milankl/StochasticRounding.jl 30 Apr 2021

While many studies have shown that low-precision arithmetic can be suitable on short-term weather forecasting timescales, our results give the first evidence that a similar low precision level can be suitable for climate.

Atmospheric and Oceanic Physics

0.02 stars / hour

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

atomistic-machine-learning/field_schnet 4 Sep 2018

SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecules and materials.

Computational Physics Chemical Physics

0.02 stars / hour

The rise of data-driven weather forecasting

198808xc/Pangu-Weather 19 Jul 2023

A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.

Atmospheric and Oceanic Physics

0.02 stars / hour