readme.md

Overview

The tools directory contains auxiliary scripts and utilities for tasks such as format conversion, data analysis, structure generation, etc. Hope you find them helpful, but you should use them with caution. If you have questions on a tool, you can try to contact the creator.

Directory Structure

The tools are grouped into three categories for now:

Category	Tools
Format Conversion	abacus2xyz, castep2exyz, cp2k2xyz, dp2xyz, exyz2pdb, gmx2exyz, gpumd_to_kaldo, mtp2xyz, orca2xyz, runner2xyz, vasp2xyz, xtd2exyz, xyz2gro
Analysis and Processing	add_groups, energy-reference-aligner, get_max_rmse_xyz, hydrogen_bond_analysis, pbc_mol, pca_sampling, perturbed2poscar, rdf_adf, select_xyz_frames, shift_energy_to_zero, split_xyz,
Miscellaneous	doc_3.3.1, for_coding, md_tersoff, vim

Packages related to GPUMD and/or NEP:

Also, there are some packages that may be useful to you.

Package	link	comment
calorine	https://gitlab.com/materials-modeling/calorine	calorine is a Python package for running and analyzing MD simulations via GPUMD. It also provides functionality for constructing and sampling NEP models via GPUMD.
GPUMD-Wizard	https://github.com/Jonsnow-willow/GPUMD-Wizard	GPUMD-Wizard is a material structure processing software based on ASE (Atomic Simulation Environment) providing automation capabilities for calculating various properties of metals. Additionally, it aims to run and analyze MD simulations using GPUMD.
gpyumd	https://github.com/AlexGabourie/gpyumd	gpyumd is a Python interface for GPUMD. It helps users generate input and process output files based on the details provided by the GPUMD documentation. It currently supports up to GPUMD-v3.3.1 and only the gpumd executable.
GPUMDkit	https://github.com/zhyan0603/GPUMDkit	GPUMDkit is a toolkit for the GPUMD and NEP. It provides a set of tools to streamline the use of common scripts in GPUMD and NEP, simplifying workflows and enhancing efficiency.
mdapy	https://github.com/mushroomfire/mdapy	The mdapy Python library provides an array of powerful, flexible, and straightforward tools to analyze atomic trajectories generated from MD simulations.
pynep	https://github.com/bigd4/PyNEP	PyNEP is a Python interface of the machine learning potential NEP used in GPUMD.
somd	https://github.com/initqp/somd	SOMD is an ab-initio molecular dynamics (AIMD) package designed for the SIESTA DFT code. The SOMD code provides some common functionalities to perform standard Born-Oppenheimer molecular dynamics (BOMD) simulations, and contains a simple wrapper to the NEP package. The SOMD code may be used to automatically build NEPs by the mean of the active-learning methodology.
NepTrain	https://github.com/aboys-cb/NepTrain	An automated toolkit for training NEP, integrating tools like GPUMD, VASP, and NEP for streamlined workflows including perturbation, active learning, single-point energy calculations, and potential training.
NepTrainKit	https://github.com/aboys-cb/NepTrainKit	NepTrainKit is a Python package for visualizing and manipulating training datasets for NEP.
NEP_Active	https://github.com/psn417/NEP_Active	NEP_Active is a python package for building the training set using active learning strategy. It follows the method of Moment Tensor Potential (MTP).
nep_maker	https://github.com/psn417/nep_maker	This Python package facilitates the construction of NEP using active learning techniques. It employs the same strategies as MTP [J. Chem. Phys. 159, 084112 (2023)] and ACE [Phys. Rev. Materials 7, 043801 (2023)].