MiMiCPy: An Efficient Toolkit for MiMiC-Based QM/MM Simulations

Journal of Chemical Information and Modeling, vol. 63, pp. 1406–1412

Abstract

MiMiC is a highly flexible, extremely scalable multiscale modeling framework. It couples the CPMD (quantum mechanics, QM) and GROMACS (molecular mechanics, MM) codes. The code requires preparing separate input files for the two programs with a selection of the QM region. This can be a tedious procedure prone to human error, especially when dealing with large QM regions. Here, we present MiMiCPy, a user-friendly tool that automatizes the preparation of MiMiC input files. It is written in Python 3 with an object-oriented approach. The main subcommand PrepQM can be used to generate MiMiC inputs directly from the command line or through a PyMOL/VMD plugin for visually selecting the QM region. Many other subcommands are also provided for debugging and fixing MiMiC input files. MiMiCPy is designed with a modular structure that allows seamless extensions to new program formats depending on the requirements of MiMiC.

Authors 9

1. Bharath Raghavan Aachen Department of Physics

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany

   Department of Physics, RWTH Aachen University, Aachen 52074, Germany

2. Florian Karl Schackert Aachen Department of Physics

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany

   Department of Physics, RWTH Aachen University, Aachen 52074, Germany

3. Andrea Levy

   École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

4. Sophia K. Johnson

   École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

5. Emiliano Ippoliti

   Forschungszentrum Jülich

   Affiliation as printed

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany

6. Davide Mandelli corresponding

   Forschungszentrum Jülich

   Affiliation as printed

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany

7. Jógvan Magnus Haugaard Olsen

   Technical University of Denmark

   Affiliation as printed

   DTU Chemistry, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark

8. Ursula Röthlisberger

   École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   Laboratory of Computational Chemistry and Biochemistry, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

9. Paolo Carloni corresponding Aachen Department of Physics

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Computational Biomedicine, Institute of Advanced Simulations IAS-5/Institute for Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich 52428, Germany

   Department of Physics, RWTH Aachen University, Aachen 52074, Germany