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Updated: Apr 28, 2026

Author Spotlight: Universal Molecular Retention with 11-Fold Expansion Microscopy
Published on: October 6, 2023
Aiming for benchmark accuracy with the many-body expansion
Ryan M Richard1, Ka Un Lao, John M Herbert
1Department of Chemistry and Biochemistry, The Ohio State University , Columbus, Ohio 43210, United States.
Fragment-based quantum chemistry methods decompose large systems into smaller calculations for efficiency. While promising, the many-body expansion requires careful implementation to avoid precision loss in large systems.
Area of Science:
- Quantum Chemistry
- Computational Chemistry
- Theoretical Chemistry
Background:
- Fragment-based quantum chemistry methods have advanced significantly over the last 15 years.
- These methods approximate large system properties using smaller subsystem calculations, often based on many-body (n-body) expansions.
- Existing methods present a language barrier due to diverse terminology and working equations.
Purpose of the Study:
- To unify diverse fragment-based quantum chemistry methods under a generalized many-body expansion formalism.
- To provide a universal energy formula encompassing traditional n-body cluster expansions and methods for macromolecules.
- To systematically classify and suggest improvements for fragment-based methods.
Main Methods:
- Developed a generalized many-body expansion formalism.
- Classified fragment-based methods based on fragment construction and higher-order interaction approximation.
- Investigated the efficacy of these methods for large systems, aiming for complete-basis CCSD(T) quality.
- Addressed basis-set superposition error using many-body counterpoise corrections and electrostatic embedding.
Main Results:
- Demonstrated that complete-basis CCSD(T) quality energies can be obtained for small clusters with reduced computation time.
- Observed that low-order n-body expansions can sometimes yield good results due to error cancellation.
- Identified erratic behavior of basis sets in larger systems and with high-order expansions.
- Highlighted potential loss-of-precision issues in large systems due to the combinatorial nature of the many-body expansion.
Conclusions:
- The generalized many-body expansion provides a unified framework for classifying and improving fragment-based methods.
- While promising for accuracy and efficiency, the many-body expansion requires careful implementation, especially for large systems.
- Numerical stability and precision loss are significant concerns, indicating that the many-body expansion is not yet a "black-box" method.
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