The dataset and code used in paper”Machine Learning the Hohenberg-Kohn Map to Molecular Excited States” For detailed information of each file, see Readme.txtWJG acknowledges financial support from the National Natural Science Foundation of China (NSFC), grant no.~22173060), the NSFC Fund for International Excellent Young Scientists (grant no.~22150610466), the Ministry of Science and Technology of the People's Republic of China (MOST) National Foreign Experts Program Fund (grant No.~ QN2021013001L), and the MOST Foreign Young Talents Program (grant no.~WGXZ2022006L). MET acknowledges support from the National Science Foundation (grant no. CHE-1955381). LV-M acknowledges support from the NYU University Research Challenge Fund. This project ...
Supplementary Information for "Autonomous data extraction from peer reviewed literature for training...
Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (P...
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instan...
The dataset and code used in paper”Machine Learning the Hohenberg-Kohn Map to Molecular Excited Stat...
Machine learning techniques are being increasingly used as flexible non-linear fitting and predictio...
International audienceTheoretical simulations of electronic excitations and associated processes in ...
This is the supporting information for the article "Machine Learning interpretation of the correlati...
Data used in the paper "Transferring Chemical and Energetic Knowledge Between Molecular Systems With...
This is the supporting information for the article "Machine-learning identified molecular fragments ...
An accurate simulation of the excited states of molecules can enable the study of many important pro...
The combination of modern scientific computing with electronic structure theory can lead to an unpre...
The combination of modern scientific computing with electronic structure theory can lead to an unpre...
In molecular quantum mechanics, mappings between molecular structures and their corresponding physic...
This is the supporting information for the article "Machine-learning identified molecular fragments ...
Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory ...
Supplementary Information for "Autonomous data extraction from peer reviewed literature for training...
Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (P...
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instan...
The dataset and code used in paper”Machine Learning the Hohenberg-Kohn Map to Molecular Excited Stat...
Machine learning techniques are being increasingly used as flexible non-linear fitting and predictio...
International audienceTheoretical simulations of electronic excitations and associated processes in ...
This is the supporting information for the article "Machine Learning interpretation of the correlati...
Data used in the paper "Transferring Chemical and Energetic Knowledge Between Molecular Systems With...
This is the supporting information for the article "Machine-learning identified molecular fragments ...
An accurate simulation of the excited states of molecules can enable the study of many important pro...
The combination of modern scientific computing with electronic structure theory can lead to an unpre...
The combination of modern scientific computing with electronic structure theory can lead to an unpre...
In molecular quantum mechanics, mappings between molecular structures and their corresponding physic...
This is the supporting information for the article "Machine-learning identified molecular fragments ...
Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory ...
Supplementary Information for "Autonomous data extraction from peer reviewed literature for training...
Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (P...
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instan...