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Found 6 papers, 3 papers with code
PubChemQC B3LYP/6-31G*//PM6 dataset: the Electronic Structures of 86 Million Molecules using B3LYP/6-31G* calculations
This article presents the "PubChemQC B3LYP/6-31G*//PM6" dataset, containing electronic properties of 85, 938, 443 molecules.
3D Molecular Geometry Analysis with 2D Graphs
Here, we propose a novel deep learning framework to predict 3D geometries from molecular graphs.
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
Ranked #1 on
3D Geometry Prediction
on Molecule3D val
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.
Ranked #1 on
Knowledge Graphs
on WikiKG90M-LSC
PubChemQC PM6: A dataset of 221 million molecules with optimized molecular geometries and electronic properties
We report on the largest dataset of optimized molecular geometries and electronic properties calculated by the PM6 method for 92. 9% of the 91. 2 million molecules cataloged in PubChem Compounds retrieved on Aug. 29, 2016.
Chemical Physics Materials Science
The second-order reduced density matrix method and the two-dimensional Hubbard model
In this paper, we establish the utility of the RDM method when employing the $P$, $Q$, $G$, $T1$ and $T2^\prime$ conditions in the two-dimension al Hubbard model case and we conduct a thorough study applying the $4\times 4$ Hubbard model employing a coefficients.
Strongly Correlated Electrons
