Fermi Sets
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Large Electron Model: A Universal Ground State Predictor
Announce Type: replace-cross Abstract: We introduce Large Electron Model, a single neural network model that produces variational wavefunctions of interacting electrons over the entire Hamiltonian parameter manifold. Our model employs the Fermi Sets architecture, a universal representation of many-body fermionic wavefunctions, which is further conditioned on Hamiltonian parameter and particle number. For interacting electrons in a two-dimensional harmonic potential, a single trained model...
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Introducing FrontierCode Raising the bar from correctness to quality Today’s coding benchmarks have established that models can write correct code. But as AI-generated code becomes the dominant path to production, correctness is now table stakes. The question that we should be asking is: can models actually write good code?
Two-component exciton condensates in an electron–hole bilayer
Abstract Macroscopic quantum coherence emerges when bosons condense into a Bose–Einstein condensate (BEC)1,2,3,4,5. Excitons are a long-sought solid-state route to high-temperature BECs with strong interactions, electrical tunability and potentially multicomponent spinor order, but conclusive evidence for equilibrium condensation has remained elusive. Here we report evidence for two-component exciton BECs in MoSe2/hBN/WSe2 electron–hole bilayers6,7,8,9 by probing the spin–valley...