{"ID":23038724,"CreatedAt":"2026-09-17T05:30:12.286776779Z","UpdatedAt":"2026-09-17T05:30:12.286776779Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.18826","arxiv_id":"2609.18826","title":"Gauge-including neural-network quantum Monte Carlo for molecules in magnetic fields","abstract":"External magnetic fields, through their coupling to orbital and spin motion, complicate the correlated electronic states and impose coordinate-dependent phases on the wavefunction, thereby making accurate electronic structure calculations substantially more demanding. Recently, neural network-based quantum Monte Carlo (NNQMC) has emerged as a highly accurate approach to study nucleus-free systems in magnetic fields. For molecular systems, however, things get more complicated as the magnetic field would introduce a rapidly varying phase in the region far from the gauge origin. Here we introduce a gauge-including phase factor that acts directly on the full many-electron wavefunction and accounts for the prescribed magnetic phase, leaving a smoother correlated residual for the network to learn. This factor greatly improves molecular translation consistency and size consistency, providing a route for studying systems in magnetic fields with NNQMC. Upon this approach, we reproduce weak-field magnetizabilities and strong-field bond contraction in \\ce{H2}. We further apply the method to selected transitions in the \\ce{CN} red and \\ce{C2} Swan systems at magnetic fields relevant to white dwarfs. The \\ce{CN} transition exhibits a much larger field-induced shift than its \\ce{C2} counterpart, suggesting its potential as a probe of white-dwarf magnetic fields.","short_abstract":"External magnetic fields, through their coupling to orbital and spin motion, complicate the correlated electronic states and impose coordinate-dependent phases on the wavefunction, thereby making accurate electronic structure calculations substantially more demanding. Recently, neural network-based quantum Monte Carlo...","url_abs":"https://arxiv.org/abs/2609.18826","url_pdf":"https://arxiv.org/pdf/2609.18826v1","authors":"[\"Chengye Lü\",\"Weizhong Fu\",\"Xin-gao Gong\",\"Hongjun Xiang\"]","published":"2026-09-16T15:32:12Z","proceeding":"cond-mat.mtrl-sci","tasks":"[\"cond-mat.mtrl-sci\"]","methods":"[]","has_code":false}