Journal Article

PUBDB-2025-04599

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Taylor-mode automatic differentiation for constructing molecular rovibrational Hamiltonian operators

Yachmenev, A. (Corresponding author)Extern*DESY* ; Vogt, E.CFEL*DESY* ; Corral, Á. F.Extern*DESY* ; Saleh, Y.CFEL*Extern*DESY*

2025
American Institute of Physics Melville, NY

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Please use a persistent id in citations: doi:10.1063/5.0287347  doi:10.3204/PUBDB-2025-04599

Abstract: 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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Contributing Institute(s):

1. CFEL-CMI (FS-CFEL-CMI)

Research Program(s):

1. 631 - Matter – Dynamics, Mechanisms and Control (POF4-631) (POF4-631)
2. Quantum-mechanical modeling of the dissociation of hydrogen bonds (101155136) (101155136)
3. HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter (2019_IVF-HIDSS-0002) (2019_IVF-HIDSS-0002)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2025

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; National-Konsortium ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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