arXiv subjects

Chuntian Wu

Publications and source records attributed to Chuntian Wu.

2 recordsLinked to original sources

arXiv2026

ARES: a plane-wave density-functional-theory code in Rust

ARES (Ab initio atomic mateRial modEling Software at JLU) is a plane-wave density-functional-theory (DFT) code written entirely in Rust as a layered 22-crate workspace. It supports norm-conserving pseudopotentials and the projector augmented-wave (PAW) method with the local-density approximation (LDA) and Perdew-Burke-Ernzerhof (PBE) functionals, Γ-only and explicit k-point sampling, smearing and mixing, forces, stress, relaxation, band structures, densities of states (DOS) and dispersion corrections; execution is serial or MPI-distributed over k-point pools, band groups and plane-wave rows. The code's distinguishing engineering choice is its validation apparatus. Every numerical component is checked against Quantum ESPRESSO driven from the same physical and numerical inputs, and the comparison is organized as numbered fixtures kept in the repository whose reference data is regenerated by a script instead of being captured once. On six materials spanning a semiconductor, three metals and two two-dimensional systems, band energies within one electronvolt of the Fermi level agree with the reference to a median below 0.4 meV. The pseudopotentials the code reads are validated against all-electron reference data as well: the median delta factor is 0.696 meV/atom over all 65 paired elements. Restrictions are stated with the capabilities: two functionals, no spin-polarized or spin-orbit path, a stress implementation with an explicit scope, and no accelerator execution.

cond-mat.mtrl-sci↗

arXiv2026

Accurately Generalizing Deep-Learning Hamiltonian Methods to Plane-Wave Density Functional Theory

Deep neural networks that predict density functional theory (DFT) Hamiltonians from material structures offer a route to efficient electronic-structure calculations. However, extending localized atomic-orbital (AO) Hamiltonian learning to plane-wave (PW) calculations with hybrid functionals requires a compact AO representation that preserves the effects of nonlocal operators. Here we propose a projectability-guided construction of nonorthogonal AO Hamiltonians from a small, selected set of PW eigenstates and eigenvalues. High-projectability states are retained, and selected higher-energy states complete the AO subspace, avoiding an empirical shift and its arbitrary contribution to the learning targets. Because the construction uses PW eigenstates and eigenvalues, it incorporates nonlocal contributions present in the reference calculation without explicitly reconstructing their AO matrix elements. Benchmarks for MoS2 and sodium phases show accurate reproduction of PW bands with smaller AO bases than real-space reconstruction. The resulting PBE and HSE Hamiltonians can be learned by equivariant neural networks, connecting PW reference calculations with electronic-structure prediction. This approach broadens the range of PW DFT calculations available for AO Hamiltonian learning and enables compact, accurate training targets for developing more widely applicable electronic-structure models.

cond-mat.mtrl-sci↗