CrySPY is a crystal structure prediction tool by utilizing first-principles calculations and a classical MD program. Only by inputting chemical composition, crystal structures can be automatically generated and searched. In ver. 0.6.1, random search, Bayesian optimization, and LAQA are available as searching algorithms. CrySPY is interfaced with VASP, Quantum ESPRESSO, and LAMMPS.
Parallel C++ Library for tensor network methods. This library provides common operations, including tensor contraction and singular value decomposition and supports a similar interface as Numpy and Scipy in Python.
GPU library for pdgemm and pzgemm, which are functions of matrix-matrix operations in ScaLAPACK.
Automatic generation tool for codes of tensor contraction. This tool can automatically generate codes of an optimal computing sequence for construction of a single tensor from a tensor network composed of tensors. Netcon algorithm proposed by Pfeifer et al. is used, and it is possible to search optimal solution quickly. Generated codes are available in Numpy and mptensor in Python.
Libxc is an library for exchange-correlation functions in the density functional theory. This has been developed for the purpose that well-tested exchange-correlation functions can be easily used in any DFT codes. In Libxc, users can find several types of exchange-correlation functions: LDA, GGA, hybrid-GGA, and meta-GGA.
Elastic is a set of python routines for calculation of elastic properties of crystals (elastic constants, equation of state, sound velocities, etc.). It is implemented as a extension to the Atomic Simulation Environment (ASE) system. There is a script providing interface to the library not requiring knowledge of python or ASE system.
Fortran codes for computing the specified k-th eigenvalue and eigenvector for generalized symmetric definite eigenvalue problems. Sylvester’s law of inertia is employed as the fundamental principle in computations, and the sparse direct linear solver (MUMPS) is used in the main routine. By inputting Hamiltonian and its overlap matrices, user can compute electron’s energy and its wave function in the specified k-th energy level.
COMmon Bayesian Optimization Library (COMBO) is an open source python library for machine learning techniques. COMBO is amenable to large scale problems, because the computational time grows only linearly as the number of candidates increases. Hyperparameters of a prediction model can be automatically learned from data by maximizing type-II likelihood.
XenonPy is a high-throughput material exploration framework based on machine learning technologies. This library can generate various chem/phys descriptors for machine learning to explore materials in virtual environment. Descriptors in matminer can be also used. Model training is done by PyTorch. Visualization tool for descriptor and transfer learning framework are also provided.
isqpr is an R package to find candidate molecules that has your desired chemical structures and chemical properties. SMILES (Simplified Molecular Input Line Entry Specification Syntax) is employed to represent chemical structures. To find candidate molecules, sequential Monte Carlo method generates new molecules, whose chemical properties are predicted by machine learning techniques.