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Updated: May 24, 2026
12:11
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
Exploring chemistry with the fragment molecular orbital method.
Dmitri G Fedorov1, Takeshi Nagata, Kazuo Kitaura
1NRI, National Institute of Advanced Industrial Science and Technology (AIST), Central 2, Umezono 1-1-1, Tsukuba, 305-8568, Japan. d.g.fedorov@aist.go.jp
Physical Chemistry Chemical Physics : PCCP
|March 14, 2012
Summary
The fragment molecular orbital (FMO) method enables efficient large-scale molecular calculations for systems like proteins. This review covers FMO
Area of Science:
- Computational Chemistry
- Quantum Chemistry
- Biochemistry
Background:
- The fragment molecular orbital (FMO) method offers near linear scaling for large molecular systems.
- FMO is crucial for biochemical applications like protein-ligand binding and drug design.
- The method is parallelized for high-performance computing.
Purpose of the Study:
- To provide a comprehensive review of the fragment molecular orbital (FMO) method.
- To summarize the historical development and advancements in FMO methodology.
- To highlight the diverse applications of FMO in computational chemistry and biochemistry.
Main Methods:
- Review of historical development of the fragment molecular orbital (FMO) method.
- Summary of FMO method development, including parallelization and interfacing with wave functions and solvent models.
- Compilation of FMO applications in various scientific domains.
Main Results:
- The fragment molecular orbital (FMO) method achieves near linear scaling for large molecular systems.
- FMO has been successfully applied in protein-ligand binding and drug design.
- Efficient parallelization of FMO supports petascale computing.
Conclusions:
- The fragment molecular orbital (FMO) method is a powerful tool for large-scale molecular simulations.
- FMO's versatility and efficiency make it suitable for complex biochemical studies.
- Continued development and application of FMO are expected in computational science.
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