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arXiv · 2601.14073

DDCCNet: Physics-enhanced Multitask Neural Networks for Data-driven Coupled-cluster

P. D. Varuna S. Pathirage·Konstantinos D. Vogiatzis
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Abstract

We present the data-driven coupled-cluster deep network (DDCCNet), a family of multitask, physics-enhanced deep learning architectures designed to predict coupled-cluster singles and doubles (CCSD) amplitudes and correlation energies from lower-level electronic structure methods. The three DDCCNet variants (termed as v1, v2, and v3) progressively incorporate architectural refinements ranging from parallel subnetworks for t_1 and t_2 amplitudes to feature-partitioned blocks and physics-enhanced intermediate prediction layers that are structured in accordance with coupled-cluster equations to enhance physical consistency and multitask learning efficiency. These models jointly learn correlated amplitude patterns while embedding symmetry and orbital-level interactions directly into the network structure. Applied to methanol conformers, CO2 clusters, and small organic molecules, DDCCNet_v2 delivered the most accurate and transferable performance, achieving chemically precise correlation energies across diverse molecular systems. Collectively, DDCCNet establishes a scalable, physically grounded framework that unifies machine learning and ab initio theory for efficient, data-driven electronic structure prediction.

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P. D. Varuna S. Pathirage, Konstantinos D. Vogiatzis. 2026-02-02. DDCCNet: Physics-enhanced Multitask Neural Networks for Data-driven Coupled-cluster. https://arxiv.org/abs/2601.14073

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