An AI system for predicting protein conformation. It is possible to predict the three-dimensional structure (folding structure) of a protein from its primary sequence (amino acid sequence). It learns hundreds of thousands of protein structure databases and uses DeepMind-based deep learning techniques to predict the conformation of new proteins from their amino acid sequences.
A tool for generating wavevector paths in band calculations of solids. It identifies high-symmetry points in reciprocal space based on the symmetry of the crystal and provides a standardized “path” connecting them. It supports various crystal structure formats (such as POSCAR and CIF) and is compatible with many electronic structure calculation software (e.g., VASP, Quantum ESPRESSO, ABINIT). A web-based interface is also available.
EPW (Electron-Phonon Wannier) is software for first-principles calculations of electron-phonon interactions. It utilizes Wannier functions to efficiently interpolate electronic and phononic states, enabling high-precision analysis of electron-phonon coupling and superconducting properties. EPW operates as an extension module of Quantum ESPRESSO (QE), using the results of electronic structure and phonon calculations as input.
A Python package for extracting structural features from point cloud and image data using the mathematical framework of persistent homology. In the field of materials science, it is used to characterize structural differences between liquids and glasses, as well as for dimensionality reduction of microscope images. It is also useful for obtaining structural descriptors for machine learning.
DIRAC (“Program for Atomic and Molecular Direct Iterative Relativistic All-electron Calculations”) is a comprehensive software package designed for performing relativistic quantum chemistry calculations on molecular systems. It supports all-electron treatments and accommodates a range of approaches, from fully relativistic four-component calculations to non-relativistic approximations.
A software package that generates high-accuracy interatomic potentials using deep learning trained on first-principles molecular dynamics data. The DeePMD model enables molecular dynamics simulations with density functional theory (DFT) accuracy at greatly reduced computational cost. It can be coupled with molecular dynamics codes such as LAMMPS, and is widely applicable to large-scale systems, high-temperature and high-pressure conditions, and the exploration of novel materials.
An application for molecular modeling and visualization. This application can be used in cooperation with other applications such as TINKER, MSMS, Firefly, GAMESS, MOPAC, and Gaussian. In particular, this application is essential to visualization of the FMO calculation in GAMESS. It also supports graphical user interface for input-file preparation, dynamic image presentation of normal-mode vibration, and visualization of energies and structures near transition states.
An electronic state solver distributed with GAMESS, the quantum chemical (QM) calculation software. Combining energy density analysis and Divide-and-Conquer (DC) method, accurate QM calculation with electronic correlation is solved in a short time. Highly accurate QM calculations for many-atom/nano-scale material can be solved when run on a high performance super computer.