Abstract:We introduce QERNEL, a foundational neural wavefunction that variationally solves families of parameterized many-electron Hamiltonians and captures their ground states throughout parameter space within a single model. QERNEL combines FiLM-based parameter conditioning with scale-efficient architectural elements -- mixture of experts and grouped-query attention, substantially improving expressivity at low computational cost. We apply QERNEL to interacting electrons in semiconductor moiré heterobilayers, training a single weight-shared model for systems of up to 150 electrons. By solving the many-electron Schrödinger equation conditioned on moiré potential depth, QERNEL captures both quantum liquid and crystal states and discovers the sharp phase transition between them, marked by abrupt changes in interaction energy and charge density. Our work establishes a foundation model for moiré quantum materials and a scalable architecture toward a Large Electron Model for solids.
| Comments: | 6 pages, 4 figures |
| Subjects: | Strongly Correlated Electrons (cond-mat.str-el); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.26018 [cond-mat.str-el] |
| (or arXiv:2604.26018v1 [cond-mat.str-el] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26018 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Khachatur Nazaryan [view email]
[v1]
Tue, 28 Apr 2026 18:00:37 UTC (5,454 KB)