Designing Strongly Correlated Quantum Phases of Matter with Foundation Neural-Network Quantum States

Designing a material with a desired property amounts to solving an inverse problem: finding the couplings of a microscopic Hamiltonian whose ground state exhibits that property. For strongly correlated quantum systems, solving this problem efficiently remains largely out of reach. To address this challenge, we present a general framework for ab initio inverse design based on Foundation Neural-Network Quantum States, a recent approach in which the ground states of a family of Hamiltonians are encoded in a single variational wave function. Because the ansatz depends explicitly on the couplings, any target property is a differentiable function of them, and the search for the right Hamiltonian reduces to gradient-based optimization in coupling space. We apply this approach to search for nonmagnetic phases of frustrated Heisenberg models on the square lattice with an increasing number of free next-nearest-neighbor couplings, aiming to identify new quantum spin liquid candidates. With a single coupling, the method recovers the known nonmagnetic window of the square $J_1$-$J_2$ Heisenberg model, whereas letting the two diagonal couplings vary independently reveals an extended nonmagnetic region connecting the square-lattice and anisotropic-triangular-lattice regimes. In a search space of eight independent couplings within a $2\times 2$ unit cell, which contains several paradigmatic frustrated spin models, the optimization spontaneously converges to the $J_1$-$J_2$-$\delta$ Heisenberg model, in which the diagonal couplings alternate between two values on neighboring plaquettes, a model recently proposed in the context of altermagnetism. Finite-size scaling up to $16\times 16$ clusters in this optimal model shows that the ground state has no magnetic, dimer, or plaquette order, establishing it as a new quantum spin liquid candidate, distinct from those previously proposed on the square lattice.