atomic-simulation-environment

Scientific Python package for solving Slater Koster tight-binding topological hamiltonian

Julia implementation of algorithm for counting primitive rings in an atomistic structure. Useful for materials simulations

A quick tutorial for modern materials science, should the reader be not familiar with it and just wishing to crack the data

The Otago Research Genetic Algorithm for Nanoclusters, Including Structural Methods and Similarity (Organisms) program is a genetic algorithm designed to globally optimise nanoclusters (also known as clusters or nanoparticles)

Fork of ASE https://gitlab.com/ase/ase with added support for performing Koopmans functional calculations with Quantum ESPRESSO

This module includes functions that can be used to simulate mechanochemical phenomena. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001436

Python class to interface with QUESTAAL Quantum codes which is a suite of electronic structure programs.

This repository contains wrapper scripts for running transition state and IRC (Intrinsic Reaction Coordinate) calculations using Sella and IRC ASE optimizers for the Sella package.

Python wrapper for GFN-FF based on F2PY. https://pypi.org/project/pygfnff

Cost-bounded quantum-mechanical discovery, ASE supercell modeling, and Ferritic Nitrocarburizing (FNC) kinetic solver for anti-fretting bearing steel (NITRO_BRG_ALLOY_000). Features VASP DFT elastic relaxation, Chromium-trapping diffusion kinetics, and thermo-mechanical stress/EHL permutation testing. Developed with Google Gemini co-pilot support.

Python wrapper for GFN0 based on F2PY. https://pypi.org/project/pygfn0

This repository contains scripts to test the performance of different path optimization appraches to compute transition states and the minimum energy paths.

GUI tool to run NEB calculation using ASE and Gaussian16 or XTB binary.

Density functional theory (DFT)-based Genetic algorithm (GA) code for structural optimization of supported nanoparticle model in a queueing system

ASE-based code to globally optimize the atomic configuration of a bulk bimetallic material.

This repository provides a custom optimizer for the Atomistic Simulation Environment (ASE), implementing a Block-BFGS (B-BFGS) algorithm.

This repository contains examples of genetic algorithm optimisations using the Organisms program that can be run on Jupyter. This can be run either on your computer or live on the web through Binder

Welcome to the CMD-Lab group.

Interfacing Fortnet with the Atomic Simulation Environment