A highly efficient framework for crystal structure exploration and property prediction dedicated to material science calculations. This application can automate the setup, execution, and analysis of the results of calculations based primarily on the density functional theory. It provides data on more than millions of crystal structures and can be used for high throughput calculations for material exploration. It also interfaces with various DFT codes (VASP, Quantum ESPRESSO, etc.).
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.
A Boltzmann transport equation solver for calculating lattice thermal conductivity based on phonon information obtained from first-principles calculations. It takes into account three-phonon interactions and enables first-principles analysis of thermal transport properties in solids, including anisotropic crystals, complex structures, and those containing defects. Tutorials and input-support tools are also provided. A tool for calculating third-order force constants (thirdorder.py) is also available on the same website.
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.
A software package for calculating lattice thermal conductivity based on phonon information obtained from first-principles calculations, including four-phonon scattering processes. It extends ShengBTE to account for four-phonon interactions that become dominant at high temperatures. The program enables quantitative analysis of the competition between three- and four-phonon interactions as well as temperature dependence, allowing for more accurate evaluation of thermal transport properties.
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 benchmark framework for evaluating general-purpose, i.e., universal, machine learning potentials, along with a leaderboard based on those evaluations. Rankings are determined by a comprehensive assessment that considers the accuracy of predicted formation energy of materials, structural relaxation, and thermal conductivity. Recently, in addition to public research institutions such as universities, major companies like Meta, Microsoft, and Google have also joined the development of universal potentials, taking top positions on the leaderboard.
An open-source Python molecular editor optimized for designing organic molecules. Built on RDKit and PyVista, it enables seamless conversion from 2D structural formulas to 3D models and advanced visualization. It also features a flexible plugin system, supporting specialized functions such as generating Gaussian/ORCA inputs, ESP mapping, and conformational searches, while allowing users to develop their own custom features.