2026-10-12T18:12:37Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33941198 2026-09-19T03:08:31Z category_1 category_146 category_915 category_873 category_734 category_931 portal_63 item_type_3 month_year_09_2026

A Physics-Constrained Synergetic Active- and Transfer-Learning Framework for Constructing a Spectroscopically Accurate Full-Dimensional Potential Energy Surface of H₂O–N₂ Complex Jia Nie (3763024) You Li (238760) Zhixia Wang (10071412) Xiaolong Zhang (470454) Hui Li (32376) Biophysics Space Science Physical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified weakly bound complexes spectroscopically relevant low previous theoretical values constrained synergetic active ab initio </ 6 × 10 driven active learning pess remains challenging 2 </ sub spectroscopically accurate full synatl therefore provides gives 10 learning framework pess ). dimensional pess – n transferable framework test set square error square deviation specific rmsds rovibrational calculations resolution spectroscopy range representation quantitative spectroscopy explicit long dimensional pes combines uncertainty attractive regions High-resolution spectroscopy of weakly bound molecular complexes requires theoretical predictions with uncertainties below 0.1 cm^–1. Achieving this level of accuracy places stringent demands on the underlying full-dimensional potential energy surfaces (PESs). However, constructing such PESs remains challenging because flexible configuration space requires dense sampling and the <i>ab initio</i> energies used for fitting must meet spectroscopic accuracy requirements. Here, we develop a physics-constrained synergetic active- and transfer-learning framework (SynATL) that combines uncertainty-driven active learning, high-level refinement via transfer learning, and an explicit long-range representation to construct spectroscopically accurate full-dimensional PESs. SynATL was used to construct a full-dimensional PES of the H₂O–N₂ complex at the post-CCSD(T) level, achieving a weighted root-mean-square error (RMSE) of 0.815 cm^–1 on the test set and sub-cm^–1 RMSEs in the spectroscopically relevant low-energy and attractive regions. Applied to rovibrational calculations of H₂O–N₂ and D₂O–N₂, the resulting PES reproduces microwave transitions with a root-mean-square deviation (RMSD) of 1.6 × 10^–4 cm^–1, about 80% lower than previous theoretical values, and gives 10^–3 cm^–1 level band-specific RMSDs for 235 assigned infrared lines across the H₂O–N₂ bending/overtone and D₂O–N₂ stretching/bending bands. SynATL therefore provides an efficient and transferable framework for full-dimensional PESs for quantitative spectroscopy of weakly bound complexes. 2026-09-18T00:00:00Z Dataset Dataset 10.1021/acs.jctc.6c01295.s001 https://figshare.com/articles/dataset/A_Physics-Constrained_Synergetic_Active-_and_Transfer-Learning_Framework_for_Constructing_a_Spectroscopically_Accurate_Full-Dimensional_Potential_Energy_Surface_of_H_sub_2_sub_O_N_sub_2_sub_Complex/33941198 CC BY-NC 4.0