The WEST code for large-scale excited-state materials simulations

We present WEST, an open-source plane-wave pseudopotential code for large-scale excited-state materials simulations, and describe its theoretical foundations, software architecture, and capabilities. WEST implements full-frequency GW, quantum defect embedding theory, the Bethe–Salpeter equation, and time-dependent density functional theory within a common algorithmic framework that avoids the explicit computation of virtual electronic states. By combining density functional and density matrix perturbation theory, low-rank representations of the dielectric screening and exact exchange, and localization techniques, WEST achieves favorable computational scaling with system size. The code supports the calculation of quasi-particle and neutral excitation energies, optical and photoluminescence spectra, excited-state forces, and non-adiabatic couplings, with interoperable workflows connecting to quantum chemistry, vibronic coupling, and quantum computing packages. A hierarchical parallelization strategy and graphics processing unit (GPU) acceleration deliver near-ideal strong scaling to thousands of GPUs, enabling accurate excited-state simulations of systems with more than a thousand atoms. Representative applications, spanning the full optical cycle of solid-state spin defects, self-trapped excitons in metal-halide perovskites, and the optical response of liquid water and ice, demonstrate the accuracy and versatility of the code across diverse material classes. The capabilities implemented in WEST establish the code as a scalable platform for predictive excited-state simulations, high-throughput materials discovery, and the generation of high-fidelity datasets for machine learning in computational materials science.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2026-10-08
DOI
https://doi.org/10.1063/5.0349651
Primary Topic
Advanced Chemical Physics Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

The WEST code for large-scale excited-state materials simulations

Jiawei Zhan, Marco Govoni, Vrindaa Somjit, Stefano Paolo Villani et al.
The Journal of Chemical Physics
Advanced Chemical Physics Studies
article

The WEST code for large-scale excited-state materials simulations

Jiawei Zhan, Marco Govoni, Vrindaa Somjit, Stefano Paolo Villani, Y Jin, Victor Wen‐zhe Yu, Giulia Galli, Siyuan Chen
article en

Abstract

We present WEST, an open-source plane-wave pseudopotential code for large-scale excited-state materials simulations, and describe its theoretical foundations, software architecture, and capabilities. WEST implements full-frequency GW, quantum defect embedding theory, the Bethe–Salpeter equation, and time-dependent density functional theory within a common algorithmic framework that avoids the explicit computation of virtual electronic states. By combining density functional and density matrix perturbation theory, low-rank representations of the dielectric screening and exact exchange, and localization techniques, WEST achieves favorable computational scaling with system size. The code supports the calculation of quasi-particle and neutral excitation energies, optical and photoluminescence spectra, excited-state forces, and non-adiabatic couplings, with interoperable workflows connecting to quantum chemistry, vibronic coupling, and quantum computing packages. A hierarchical parallelization strategy and graphics processing unit (GPU) acceleration deliver near-ideal strong scaling to thousands of GPUs, enabling accurate excited-state simulations of systems with more than a thousand atoms. Representative applications, spanning the full optical cycle of solid-state spin defects, self-trapped excitons in metal-halide perovskites, and the optical response of liquid water and ice, demonstrate the accuracy and versatility of the code across diverse material classes. The capabilities implemented in WEST establish the code as a scalable platform for predictive excited-state simulations, high-throughput materials discovery, and the generation of high-fidelity datasets for machine learning in computational materials science.

The Journal of Chemical PhysicsVol. 165(14)
University of Modena and Reggio Emilia (IT), Argonne National Laboratory (US), University of Chicago (US), Flatiron Institute (US)
U.S. Department of Energy, University of Chicago, Midwest Integrated Center for Computational Materials, Office of Science, Basic Energy Sciences, Argonne National Laboratory
Openalex Percentile: Top 46%
Advanced Chemical Physics Studies
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