Data Repository Search

Repository Name
Category	Electronic structure (solid state physics)(19) Electronic structure (quantum chemistry)(1) Molecular dynamics(1) Strong correlation/effective models(19) Continuum models(0) Machine learning(7) Quantum computing(1) Others(3)
Keywords
DOI
Author Name
ORCID
Initial letter
Registration Date

Recently Added

Comparison of intermediate-range order in GeO2 glass: molecular dynamics using machine-learning interatomic potential vs. reverse Monte Carlo fitting to experimental data: Training data for neural network potential of amorphous GeO2, as well as resulting molecular dynamics data and structure characterization

Matrix Collection in Material Science: Collection of sparse Hamiltonian matrices that appears in material science, such as electronic structure and quantum lattice models. Several data sets contain eigenvalues and eigenvectors, as well as the matrix data.

Robust analytic continuation using sparse modeling approach imposed by semi-positive definiteness for multi-orbital systems: A dataset of the spectral functions and the corresponding Green's functions for multiple orbital electron systems used for numerical demonstrations of analytic continuation methods

Monte Carlo database on quantum spin models: Collection of Monte Carlo simulation results on quantum spin models. Currently includes S=1/2 and S=1 antiferromagnetic Heisenberg model in 1 and 2 dimensions.

Moller Gallery: Moller is a program to support high-throughput computations, provided as a part of the HTP-tools package. The use of machine learning for predicting material properties and designing substances, known as materials informatics, has gained considerable attention in recent years. The accuracy of machine learning depends heavily on the preparation of appropriate training data. Therefore, the development of tools and environments for the rapid generation of training data is expected to contribute significantly to the advancement of research in materials informatics.
Moller generates batch job scripts for supercomputers and clusters, allowing parallel execution of programs under a series of computational conditions, such as parameter parallelism. This repository provides sample inputs and outputs that illustrate the use of Moller.

Data set for Noise reduction of coherent diffraction image data using deep learning method: Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional background processing. In this data repository, sample codes, training dataset and learned model for deep learning method which was used to validate noise reduction techniques are provided.

Electronic band structure change with structural transition of buckled Au<sub>2</sub>X monolayers induced by strain: This study investigates the structural transitions of η ↔ θ and the changes in electronic band structures of Au₂X (X=S, Se, Te, Si, Ge) and Au₄SSe induced by strain.
This database provides the input files and the results of the DFT calculations by OpenMX.

betap-ET2ICl2: Project URL：https://isspns-gitlab.issp.u-tokyo.ac.jp/k-yoshimi/betap-ET2ICl2
Update Date：2024-11-10 06:14:40

GeO2_glass_NNP: Project URL：https://isspns-gitlab.issp.u-tokyo.ac.jp/skasamatsu/GeO2_glass_NNP
Update Date：2024-11-06 18:38:37

spmac_multi: Project URL：https://isspns-gitlab.issp.u-tokyo.ac.jp/y-motoyama/spmac_multi
Update Date：2024-09-04 17:45:44

Monte Carlo database on quantum spin models: Project URL：https://isspns-gitlab.issp.u-tokyo.ac.jp/kawashima/monte-carlo-database-on-quantum-spin-models
Update Date：2024-06-20 13:37:40

moller-gallery: Project URL：https://isspns-gitlab.issp.u-tokyo.ac.jp/htp-tools-dev/moller-gallery
Update Date：2024-06-10 16:27:30