Skip to main content

MACE: MetAl Complexes Embedding

MACE is an open source toolkit for the automated screening and discovery of octahedral and square-planar mononuclear complexes. MACE is developed by the Evgeny Pidko Group in the Department of Chemical Engineering at TU Delft. The software generates all possible configurations for square-planar and octahedral metal complexes and atomic 3D coordinates suitable for quantum-chemical computations. It supports ligands of high complexity and can be used for the development of a massive computational pipelines aimed at solving problems of homogenious catalysis.

For more details see the GitHub page.

Installation

conda

We highly recommend to install MACE via the conda package management system. The following command will create new conda environment with Python 3.7, RDKit 2020.09, and the latest version of MACE:

> conda create -n mace epic-mace -c grimgenius

The reason for the strong preference for installation via conda is that only the RDKit 2020.09 version ensures failure- and error-free operation of the MACE package. Earlier versions do not support dative bonds, and in later versions there are significant changes in the embedding and symmetry processing algorithms which are not well compatible with the MACE's underlying algorithms.

pip

MACE can be installed via pip (ref):

> pip install epic-mace

However, we strongly recommend installation via conda, since the earliest available RDKit version on PyPI is 2022.03 which does not ensure the stable operation of the MACE package.

In extreme cases, one can install MACE via pip to the conda environment with preinstalled RDKit 2020.09:

> conda create -n mace python=3.7 rdkit=2020.09.1 -c rdkit
> conda activate mace
> pip install epic-mace

Please note, that setup.py does not contain rdkit in the requirements list to avoid possible conflicts between conda and pip RDKit installations.

Download files

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

Source Distribution

epic_mace-0.4.1.tar.gz (33.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

epic_mace-0.4.1-py3-none-any.whl (36.2 kB view details)

Uploaded Python 3

File details

Details for the file epic_mace-0.4.1.tar.gz.

File metadata

  • Download URL: epic_mace-0.4.1.tar.gz
  • Upload date:
  • Size: 33.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.8

File hashes

Hashes for epic_mace-0.4.1.tar.gz
Algorithm Hash digest
SHA256 71721b7467d5b51a331af82690ffaecf28c68ed9e658f1538046b2bb5a399300
MD5 3e6d09dc8abeeb6fb7a4f976703d4271
BLAKE2b-256 abe8e04f6c1dd66f096e59dccbf8c9936eba5f628a264ca9db630665b9a5a3b8

See more details on using hashes here.

File details

Details for the file epic_mace-0.4.1-py3-none-any.whl.

File metadata

  • Download URL: epic_mace-0.4.1-py3-none-any.whl
  • Upload date:
  • Size: 36.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.8

File hashes

Hashes for epic_mace-0.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 63b08894e7d82e6cab9b2472c6a5ddaf45ca61d92344fcb4cdaf6edb9ab3602b
MD5 3629b20a60a0deb5bf9dc35d37ac644a
BLAKE2b-256 ffa7b3da8a07831c10e9d1ee936644b2a1adced433301b0421e03417ea0bb740

See more details on using hashes here.

Supported by

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