Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 149, Issue 14, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.5054310
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Funding
- Department of Energy [DE-SC0015997]
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A machine learning method is proposed for representing the elements of diabatic potential energy matrices (PEMs) with high fidelity. This is an extension of the so-called permutation invariant polynomial-neural network (PIP-NN) method for representing adiabatic potential energy surfaces. While for one-dimensional irreducible representations the diagonal elements of a diabatic PEM are invariant under exchange of identical nuclei in a molecular system, the off-diagonal elements require special symmetry consideration, particularly in the presence of a conical intersection. A multiplicative factor is introduced to take into consideration the particular symmetry properties while maintaining the PIP-NN framework. We demonstrate here that the extended PIP-NN approach is accurate in representing diabatic PEMs, as evidenced by small fitting errors and by the reproduction of absorption spectra and product branching ratios in both H2O((X) over tilde/(B) over tilde) and NH3((X) over tilde/(A) over tilde) non-adiabatic photodissociation. Published by AIP Publishing.
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