From 5188e4d6ae5d65e2a90f672e819a7e0380a947e7 Mon Sep 17 00:00:00 2001 From: unknown Date: Tue, 26 Nov 2024 17:41:04 +0800 Subject: [PATCH 1/2] update README --- README.md | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index b994ef7d..70748bb7 100644 --- a/README.md +++ b/README.md @@ -39,16 +39,9 @@ This software is distributed under the GNU General Public License (GPL) version * For developers only: * [The developer guide](developers) -## Python packages related to GPUMD and/or NEP: +## Tools -| Package | link | comment | -| --------------------- | --------------------------------- | ---------------------------------- | -| `calorine` | https://gitlab.com/materials-modeling/calorine | `calorine` is a Python package for running and analyzing molecular dynamics (MD) simulations via GPUMD. It also provides functionality for constructing and sampling neuroevolution potential (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 molecular dynamics (MD) simulations using GPUMD. | -| `gpyumd` |https://github.com/AlexGabourie/gpyumd | `gpyumd` is a Python3 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. | -| `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 Molecular Dynamics (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 Neuroevolution Potential (NEP) package. The SOMD code may be used to automatically build NEPs by the mean of the active-learning methodology. | +Various tools for `GPUMD` and `NEP` can be found in [tools](./tools/readme.md). ## Citations @@ -123,4 +116,3 @@ arXiv:2404.13694 [cond-mat.mtrl-sci] [17] Penghua Ying, Wenjiang Zhou, Lucas Svensson, Esmée Berger, Erik Fransson, Fredrik Eriksson, Ke Xu, Ting Liang, Jianbin Xu, Bai Song, Shunda Chen, Paul Erhart, Zheyong Fan, [Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials](https://arxiv.org/abs/2409.04430), arXiv:2409.04430 [cond-mat.mtrl-sci] - From 1937248414699d0548b0929e29be33b0a7311f92 Mon Sep 17 00:00:00 2001 From: unknown Date: Tue, 26 Nov 2024 17:41:47 +0800 Subject: [PATCH 2/2] update README --- tools/readme.md | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/tools/readme.md b/tools/readme.md index c33e219d..315a20af 100644 --- a/tools/readme.md +++ b/tools/readme.md @@ -35,4 +35,17 @@ | vasp2xyz | Yanzhou Wang | yanzhowang@gmail.com | Get `train.xyz` from `VASP` outputs. | | vim | Ke Xu | twtdq@qq.com | Highlight GPUMD grammar in `vim`. | | xyz2gro | Who? | | Convert `xyz` file to `gro` file. | -| [NepTrainKit](https://github.com/aboys-cb/NepTrainKit) | Chengbing Chen| 1747193328@qq.com | NEP data visualization interface program. | + + + +## Python packages related to GPUMD and/or NEP: + +| Package | link | comment | +| -------------- | ---------------------------------------------- | ------------------------------------------------------------ | +| `calorine` | https://gitlab.com/materials-modeling/calorine | `calorine` is a Python package for running and analyzing molecular dynamics (MD) simulations via GPUMD. It also provides functionality for constructing and sampling neuroevolution potential (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 molecular dynamics (MD) simulations using GPUMD. | +| `gpyumd` | https://github.com/AlexGabourie/gpyumd | `gpyumd` is a Python3 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. | +| `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 Molecular Dynamics (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 Neuroevolution Potential (NEP) package. The SOMD code may be used to automatically build NEPs by the mean of the active-learning methodology. | +| `NepTrainKit` | https://github.com/aboys-cb/NepTrainKit | `NepTrainKit` is a Python package for visualizing and manipulating training datasets for NEP. |