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Published on: September 5, 2019
Permutation invariant polynomial neural network based diabatic ansatz for the (E + A) × (e + a) Jahn-Teller and
Yafu Guan1, David R Yarkony2, Dong H Zhang1
1State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People's Republic of China.
The permutation invariant polynomial neural network (PIP-NN) approach constructs a quasi-diabatic Hamiltonian for systems with non-Abelian symmetries. This method accurately reproduces electronic structure data for the H3+ molecule, including conical intersections.
Area of Science:
- Quantum Chemistry
- Computational Chemistry
- Theoretical Chemistry
Background:
- Constructing accurate diabatic Hamiltonians is crucial for understanding molecular systems with complex electronic structures.
- Systems exhibiting non-Abelian symmetries, such as Jahn-Teller and Pseudo-Jahn-Teller effects, pose significant challenges for traditional methods.
- Existing methods may lack the flexibility to capture the intricate details of conical intersections.
Purpose of the Study:
- To develop a novel, symmetry-adapted neural network-based diabatic ansatz for constructing quasi-diabatic Hamiltonians.
- To apply this approach to the H3+ molecular ion, a well-known system with (E + A) × (e + a) Jahn-Teller and Pseudo-Jahn-Teller interactions.
- To demonstrate the capability of the permutation invariant polynomial neural network (PIP-NN) approach in accurately representing complex potential energy surfaces.
Main Methods:
- Employing the permutation invariant polynomial neural network (PIP-NN) approach to build a quasi-diabatic Hamiltonian.
- Utilizing symmetric polynomial expansions with neural network functions taking permutation-invariant polynomials as input.
- Applying the method to the H3+ system to model its electronic structure.
Main Results:
- The PIP-NN diabatic ansatz successfully preserves the correct non-Abelian symmetries of the system.
- The approach provides functional flexibility, accurately reproducing ab initio electronic structure data, including adiabatic energies, energy gradients, and derivative couplings.
- An excellent description of the local topology of the conical intersection seam was achieved for H3+.
Conclusions:
- The PIP-NN based diabatic ansatz offers a powerful and flexible tool for constructing symmetry-adapted quasi-diabatic Hamiltonians.
- This method enables accurate reproduction of complex electronic structure data and conical intersection topologies.
- The developed ansatz is a significant step towards diabatizing more complicated molecular systems with intricate symmetries.
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