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024 7 _ |a 10.1063/5.0287347
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100 1 _ |a Yachmenev, Andrey
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245 _ _ |a Taylor-mode automatic differentiation for constructing molecular rovibrational Hamiltonian operators
260 _ _ |a Melville, NY
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|b American Institute of Physics
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520 _ _ |a We present an automated framework for constructing Taylor series expansions of rovibrational kinetic and potential energy operators for arbitrary molecules, internal coordinate systems, and molecular frame embedding conditions. Expressing operators in a sum-of-products form allows for computationally efficient evaluations of matrix elements in product basis sets. Our approach uses automatic differentiation tools from the Python machine learning ecosystem, particularly the JAX library, to efficiently and accurately generate high-order Taylor expansions of rovibrational operators.
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536 _ _ |a Quantum-mechanical modeling of the dissociation of hydrogen bonds (101155136)
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700 1 _ |a Vogt, Emil
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700 1 _ |a Corral, Álvaro Fernández
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700 1 _ |a Saleh, Yahya
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773 _ _ |a 10.1063/5.0287347
|g Vol. 163, no. 7, p. 072501
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