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.
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.