Deep neural networks for density functional theory Hamiltonian.
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Updated
May 22, 2024 - Python
Deep neural networks for density functional theory Hamiltonian.
Quantum Visualization Interacting Toolkit for Ab-initio Simulations
Tight-binding package written in Julia
A solver for the coupled and decoupled electron and phonon Boltzmann transport equations.
Extended DeepH (xDeepH) method for magnetic materials.
Quantum Monte Carlo package, TurboRVB
Sparse Gaussian Process Potentials
Provides basic data structures and helpful functions for manipulating structures, generating input files, pre-running error checks, etc.
Open-source first-principles computational toolkit for the efficient calculation of the strength of materials in 1D, 2D, and 3D materials at both zero and finite temperatures
Neural network class for molecular dynamics to predict potential energy, forces and non-adiabatic couplings.
Python wrappers for TurboRVB
SHRY (Suite for High-throughput generation of models with atomic substitutions implemented by python) is a tool for generating unique ordered structures corresponding to a given disordered structure.
Hop.jl has been renamed to HopTB.jl and moved to https://github.com/HopTB/HopTB.jl
VASP input files required to conduct ab initio MD simulation of a short polyurethane chain.
symbolic collective variable definitions and automation of metadynamics
A Modern DFT + DMFT Computation Framework
A Julia package that provides operations of a database with pseudopotential datasets
Study of molecular motion of Glycerol using NMR modeling and simulations
This repository contains all material related to the project done as a part of the course Computational Systems Biology (BT5240) in the Spring 2020 semester.
The command-line interface of Express.jl
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