Publication date
April 2023
DOIAbstract
initial release for the upcoming publication Encrypted machine learning of molecular quantum properties J Weinreich, GF von Rudorff, OA von Lilienfeld arXiv preprint arXiv:2212.0432
initial release for the upcoming publication Encrypted machine learning of molecular quantum properties J Weinreich, GF von Rudorff, OA von Lilienfeld arXiv preprint arXiv:2212.0432
Designing molecules and materials with desired properties is an important prerequisite for advancing...
The identification and use of structure-property relationships lies at the heart of the chemical sci...
In molecular quantum mechanics, mappings between molecular structures and their corresponding physic...
input data to reproduce the calculations performed as in the repositoty: https://github.com/janweinr...
Data used in the numerical experiments for the publication "Explainable Quantum Machine Learning" (a...
Initial release of data and code for paper "Quantum Embeddings of Classical Data for Quantum Machine...
Raw data for the manuscript "Provably efficient machine learning for quantum many-body problems"
The dataset and code used in paper”Machine Learning the Hohenberg-Kohn Map to Molecular Excited Stat...
Raw data for the manuscript "Provably efficient machine learning for quantum many-body problems"
This repository provides the source code for some analytical and numerical implementations of the Ph...
This is a collection of files containing the matlab code for generating the dataset, the dataset and...
This document contains the supporting information published together with the article "Quantum Chemi...
This repository contains two labs which put into practice Quantum Machine Learning techniques.If you...
Models that combine quantum mechanics (QM) with machine learning (ML) promise to deliver the accurac...
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instan...
Designing molecules and materials with desired properties is an important prerequisite for advancing...
The identification and use of structure-property relationships lies at the heart of the chemical sci...
In molecular quantum mechanics, mappings between molecular structures and their corresponding physic...
input data to reproduce the calculations performed as in the repositoty: https://github.com/janweinr...
Data used in the numerical experiments for the publication "Explainable Quantum Machine Learning" (a...
Initial release of data and code for paper "Quantum Embeddings of Classical Data for Quantum Machine...
Raw data for the manuscript "Provably efficient machine learning for quantum many-body problems"
The dataset and code used in paper”Machine Learning the Hohenberg-Kohn Map to Molecular Excited Stat...
Raw data for the manuscript "Provably efficient machine learning for quantum many-body problems"
This repository provides the source code for some analytical and numerical implementations of the Ph...
This is a collection of files containing the matlab code for generating the dataset, the dataset and...
This document contains the supporting information published together with the article "Quantum Chemi...
This repository contains two labs which put into practice Quantum Machine Learning techniques.If you...
Models that combine quantum mechanics (QM) with machine learning (ML) promise to deliver the accurac...
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instan...
Designing molecules and materials with desired properties is an important prerequisite for advancing...
The identification and use of structure-property relationships lies at the heart of the chemical sci...
In molecular quantum mechanics, mappings between molecular structures and their corresponding physic...
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