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Graph Theoretic Molecular Fragmentation for Multidimensional Potential Energy Surfaces Yield an Adaptive and General
Xiao Zhu1, Srinivasan S Iyengar1
1Department of Chemistry and Department of Physics, Indiana University, 800 E. Kirkwood Avenue, Bloomington 47405, Indiana, United States.
Journal of Chemical Theory and Computation
|August 22, 2022
Summary
This study introduces neural networks and k-means clustering for efficient molecular fragmentation, enabling accurate post-Hartree-Fock energy calculations at a fraction of the cost for ab initio molecular dynamics.
Area of Science:
- Computational Chemistry
- Quantum Chemistry
- Machine Learning in Chemistry
Background:
- Molecular fragmentation methods are crucial for accurate electronic structure calculations of large systems.
- Previous graph-theoretic approaches enabled efficient ab initio molecular dynamics (AIMD) and potential energy surface calculations.
- Existing methods achieved coupled cluster (CCSD) and Møller-Plesset perturbation theory (MP2) accuracy at density functional theory (DFT) cost.
Purpose of the Study:
- To develop a novel family of neural networks for efficient representation of post-Hartree-Fock energy contributions in molecular fragments.
- To introduce a k-means-based tessellation strategy for optimized training data selection in high-dimensional molecular spaces.
- To reduce computational cost for accurate AIMD and potential energy surface extrapolation.
Main Methods:
- A graph-theoretic framework for molecular fragmentation into overlapping subsystems (simplexes).
- Parallel neural networks trained to represent post-Hartree-Fock energy contributions for each fragment.
- A multidimensional k-means clustering algorithm for efficient training data selection, reducing data needs by up to 90%.
Main Results:
- The developed neural networks accurately extrapolate potential energy surfaces for molecular fragments.
- The graph-theoretic procedure combined fragment energies to yield full system energies for AIMD trajectories.
- The approach achieved over a tenfold reduction in computational cost compared to standard fragmentation methods.
Conclusions:
- This hybrid approach, combining graph theory, neural networks, and k-means clustering, significantly accelerates accurate electronic structure calculations.
- The method is particularly effective for achieving coupled cluster accuracy with growing fragment sizes and capturing nonlocal interactions.
- The developed strategy offers a computationally efficient pathway for complex molecular dynamics simulations and potential energy surface mapping.
