Metal–organic frameworks (MOFs) are a class of hybrid porous materials that have gained
prominence as a noteworthy material with varied applications. Currently, MOFs are in …

Recent work has applied supervised deep learning to derive continuous symmetry
transformations that preserve the data labels and to obtain the corresponding algebras of …

Deep learning was recently successfully used in deriving symmetry transformations that
preserve important physics quantities. Being completely agnostic, these techniques …

Machine learning algorithms are heavily relied on to understand the vast amounts of data
from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data …

Recent work has used deep learning to derive symmetry transformations, which preserve
conserved quantities, and to obtain the corresponding algebras of generators. In this letter …

We provide a novel Neural Network architecture that can: i) output R-matrix for a given
quantum integrable spin chain, ii) search for an integrable Hamiltonian and the …

A fundamental task in data science is the discovery, description, and identification of any
symmetries present in the data. We developed a deep learning methodology for the …

This work presents a novel approach for the optimization of dynamic systems on finite-
dimensional Lie groups. We rephrase dynamic systems as so-called neural ordinary …

Editor: J. Hisano Recent work has applied supervised deep learning to derive continuous
symmetry transformations that preserve the data labels and to obtain the corresponding …

In this study, we propose a data-driven, deep-learning-based Machine-Learning Symmetry
Discovery (MLSD) algorithm to automate the discovery of continuous Lie group symmetries …