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Reformulation of All ONIOM-Type Molecular Fragmentation Approaches and Many-Body Theories Using Graph-Theory-Based

Srinivasan S Iyengar1, Timothy C Ricard1, Xiao Zhu1

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We developed a graph-theory approach for ONIOM molecular fragmentation, enabling accurate quantum chemistry calculations at lower computational costs for various applications, including machine learning and quantum computing.

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Area of Science:

  • Computational Chemistry
  • Theoretical Chemistry
  • Quantum Mechanics

Background:

  • ONIOM (Our own N-layered Integrated molecular Orbital and molecular Mechanics) is a widely used hybrid method for molecular modeling.
  • Molecular fragmentation methods are essential for studying large systems but often face computational limitations.
  • Accurate simulations of chemical systems require advanced computational techniques.

Purpose of the Study:

  • To reformulate all ONIOM-based molecular fragmentation methods using graph theory.
  • To demonstrate the broad applicability of this new approach across various computational chemistry problems.
  • To establish a robust and accurate framework for advanced molecular simulations.

Main Methods:

  • Graph-theory-based reformulation of ONIOM fragmentation.
  • Application to Ab Initio Molecular Dynamics (AIMD) and Density Functional Theory (DFT) calculations.
  • Integration with machine learning protocols and quantum computing algorithms.

Main Results:

  • Achieved accurate post-Hartree-Fock Ab Initio Molecular Dynamics (AIMD) at DFT cost for medium-sized systems.
  • Enabled hybrid DFT condensed-phase studies at the cost of pure density functionals.
  • Facilitated reduced-cost on-the-fly large basis gas-phase and condensed-phase AIMD.
  • Enabled post-Hartree-Fock potential surfaces at DFT cost for quantum nuclear effects.
  • Developed novel transfer machine learning protocols.

Conclusions:

  • The graph-theory reformulation provides a robust and accurate method for ONIOM-based molecular fragmentation.
  • This approach significantly reduces computational costs for complex chemical simulations.
  • The framework has broad implications for advancing computational chemistry, machine learning, and quantum computing.