Skip to main content

Some useful tools related to Amber and DP.

Project description

dpamber

Some useful tools related to Amber and DP.

Installation

pip install dpamber

Tools

corr: generating data for DPRc models

DOI:10.1021/acs.jctc.1c00201 Citations

corr tool generates DeePMD-kit training data for DPRc from AMBER sander low-level QM/MM data and high-level data. For details of DPRc, read the DPRc paper.

Before using this tool, one need to prepare low-level and high-level QM/MM data:

$$ E_\text{hl}(\mathbf R)=E_\text{hl,QM}(\mathbf R)+E_\text{hl,QM/MM}(\mathbf R)+E_\text{MM}(\mathbf R) $$

$$ E_\text{ll}(\mathbf R)=E_\text{ll,QM}(\mathbf R)+E_\text{ll,QM/MM}(\mathbf R)+E_\text{MM}(\mathbf R) $$

Low-level and high-level data should use the same coordinate and the same MM method, but different QM methods. So, the correction energy for training will be

$$ \Delta E (\mathbf R) = E_\text{hl}(\mathbf R) - E_\text{ll}(\mathbf R) = (E_\text{hl,QM}(\mathbf R) - E_\text{ll,QM}(\mathbf R)) + (E_\text{hl,QM/MM}(\mathbf R) - E_\text{ll,QM/MM}(\mathbf R)) $$

An example of the command is

dpamber corr --cutoff 6. --qm_region ":1" --parm7_file some_param.param7 --nc some_coord.nc --hl high_level --ll low_level --out dataset

where --cutoff takes cutoff radius of the QM/MM interaction for training. --qm_region takes AMBER mask format for the QM region. --parm7_file and --nc take the PARM7 file and the trajectory (NetCDF) file, respectively. --ll and --hl are the prefixes of low-level and high-level files, including the mdout file (.mdout), the mden file (.mden) and the mdfrc file (.mdfrc). The output dataset directory should be put in --out.

See details from dpamber corr -h.

devi: calculate model deviation

devi can be used to calculate the model deviation of a given trajectory. You need to install DeePMD-kit using

pip install dpamber[dpgpu]

See dpamber devi -h for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dpamber-0.4.0.tar.gz (300.8 kB view details)

Uploaded Source

Built Distribution

dpamber-0.4.0-py3-none-any.whl (16.1 kB view details)

Uploaded Python 3

File details

Details for the file dpamber-0.4.0.tar.gz.

File metadata

  • Download URL: dpamber-0.4.0.tar.gz
  • Upload date:
  • Size: 300.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for dpamber-0.4.0.tar.gz
Algorithm Hash digest
SHA256 a3ed36644962c9baf65ca690210cb0d84f1f3b211108faf3fbadae9648dc6630
MD5 6b38516268224de59418001677119ae0
BLAKE2b-256 a9de87ffe2157d65b5a6762a8e36eebba1a21506e802395106b61bb64a0d75c7

See more details on using hashes here.

File details

Details for the file dpamber-0.4.0-py3-none-any.whl.

File metadata

  • Download URL: dpamber-0.4.0-py3-none-any.whl
  • Upload date:
  • Size: 16.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for dpamber-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 fb6ba9f0b0ca0f90238307155de501d242d724ec19e01978b74bf12de9e10b07
MD5 95c785659f26ddc39d03fd80f0d09f3b
BLAKE2b-256 5ddd8843cc354943ae627d860aeeb338737bdba7a0d05b19eb4ce65b0d8b30cd

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page