Zhanghao Zhouyin

A structural change involving more than ten thousand atoms turned out to be governed by just two collective motions.

Moiré Phonon Condensation in Magic-Angle Twisted Bilayer Graphene

A neural network can read only a material’s local structure and predict its quantum operators directly — reaching near first-principles accuracy while staying local enough to scale toward whole devices.

Learning local equivariant representations for quantum operators (SLEM)

Neural quantum states can act as the impurity solver inside quantum embedding, matching exact diagonalization on correlated models — where the real bottleneck is how precisely you sample observables, not training the network.

Neural-Quantum-States Impurity Solver for Quantum Embedding Problems

Trained only on first-principles eigenvalues, a deep-learning tight-binding model predicts the electronic structure of structures it has never seen — making million-atom, finite-temperature electronic simulations feasible.

Deep learning tight-binding approach for large-scale electronic simulations (DeePTB)

Making the non-equilibrium Green’s function method differentiable end to end turns a quantum transport simulator into something you can optimize through — providing gradients for sensitivity analysis and inverse design.

Automatic differentiable nonequilibrium Green's function formalism (AD-NEGF)