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Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
Published on: May 29, 2021
Multilevel X-Pol: a fragment-based method with mixed quantum mechanical representations of different fragments
Yingjie Wang1, Carlos P Sosa, Alessandro Cembran
1Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, USA.
The Journal of Physical Chemistry. B
|March 21, 2012
Summary
The explicit polarization (X-Pol) method now supports multilevel calculations, enabling different quantum mechanical theories for various system fragments. This approach enhances computational efficiency for large molecular systems in solution.
Area of Science:
- Computational chemistry
- Quantum mechanics
- Molecular modeling
Background:
- The explicit polarization (X-Pol) method is a fragment-based quantum mechanical approach for large systems.
- Current X-Pol methods treat all fragments with a single electronic structure theory.
Purpose of the Study:
- To extend the X-Pol method to a multilevel representation (multilevel X-Pol).
- To enable the use of different electronic structure methods for different fragments within a system.
Main Methods:
- Implementation of multilevel X-Pol in a modified Gaussian 09 package.
- Coupling interfragment electrostatic interactions using response density (generalized density).
- Application to hydrogen bonding complexes with varied theoretical treatments for fragments.
Main Results:
- Demonstrated the feasibility of applying high-level theories (e.g., M06, MP2, coupled cluster) to specific fragments.
- Utilized lower-level, efficient methods (e.g., Hartree-Fock, B3LYP) for other fragments.
- Successfully modeled electrostatic interactions between fragments at different levels of theory.
Conclusions:
- Multilevel X-Pol provides a computationally efficient way to study large molecular systems.
- Allows for targeted high-level treatment of critical regions (e.g., active sites) while using less expensive methods for the environment.
- This method is valuable for accurately modeling complex systems in solution, such as enzymes and biomolecules.
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