The subject of the involved phase transition in solid materials has formed not only the basis of materials technology but also the central issue of solid-state chemistry for centuries. The ability to design and control the required changes in physical properties within phase transition becomes key prerequisite for the modern functionalized materials. Herein, we have experimentally achieved the high thermoelectric performance (ZT value reaches 1.5 at 700 K) and reversible p-n-p semiconducting switching integrated in a dimetal chalcogenide, AgBiSe(2) during the continuous hexagonal-rhombohedral-cubic phase transition. The clear-cut evidences in temperature-dependent positron annihilation and Raman spectra confirmed that the p-n-p switching is derived from the bimetal atoms exchange within phase transition, whereas the full disordering of bimetal atoms after the bimetal exchange results in the high thermoelectric performance. The combination of p-n-p switching and high thermoelectric performance enables the dimetal chalcogenides perfect candidates for novel multifunctional electronic devices. The discovery of bimetal atoms exchange during the phase transition brings novel phenomena with unusual properties which definitely enrich solid-state chemistry and materials science.

The interpolative separable density fitting (ISDF) is an efficient and accurate low-rank decomposition method to reduce the high computational cost and memory usage of the Hartree-Fock exchange (HFX) calculations with numerical atomic orbitals (NAOs). In this work, we present a machine learning K-means clustering algorithm to select the interpolation points in ISDF, which offers a much cheaper alternative to the expensive QR factorization with column pivoting (QRCP) procedure. We implement this K-means-based ISDF decomposition to accelerate hybrid functional calculations with NAOs in the HONPAS package. We demonstrate that this method can yield a similar accuracy for both molecules and solids at a much lower computational cost. In particular, K-means can remarkably reduce the computational cost of selecting the interpolation points by nearly two orders of magnitude compared to QRCP, resulting in a speedup of ∼10 times for ISDF-based HFX calculations.

HONPAS is an ab initio electronic structure program for linear scaling or O(N) first-principles calculations of large and complex systems using standard norm-conserving pseudopotentials, numerical atomic orbitals (NAOs) basis sets, and periodic boundary conditions. HONPAS is developed in the framework of the SIESTA methodology and focuses on the development and implementation of efficient O(N) algorithms for ab initio electronic structure calculations. The Heyd-Scuseria-Ernzerhof (HSE) screened hybrid density functional has been implemented using a NAO2GTO scheme to evaluate the electron repulsion integrals (ERIs) with NAOs. ERI screening techniques allow the HSE functional calculations to be very efficient and scale linearly. The density matrix purification algorithms have been implemented, and the PSUTC2 and SUTC2 methods have been developed to deal with spin unrestricted systems with or without predetermined spin multiplicity, respectively. After the self-consistent field (SCF) process, additional O(N) post-SCF calculations for frontier molecular orbitals and maximally localized Wannier functions are also developed and implemented. Finally, an O(N) method based on the density matrix perturbation theory has been proposed and implemented to treat electric field in solids. This article provides an overall introduction to capabilities of HONPAS and implementation details of different O(N) algorithms.