Authors
Sudarshan Vijay, Martijn Marsman, Georg Kresse, Martin Schlipf
Published in
Journal of physics. Condensed matter : an Institute of Physics journal. Volume 38. Issue 41. Oct 20, 2026. Epub Oct 20, 2026.
Abstract
Implementing novel features and experimental algorithms into widely adopted density functional theory (DFT) codes is frequently hindered by complex legacy architectures and the use of compiled languages such as Fortran. These production codes, while optimized for high-performance computing clusters, present significant hurdles for software development and rapid prototyping, often requiring deep expertise in the code's internal structure to modify. To address this challenge, we present a Python plugin infrastructure for the Viennaab-initioSimulation Package (VASP) that combines computational efficiency with the flexibility of high-level scripting. Our architecture uses a C++ intermediate layer andto expose VASP data asarrays via shared memory buffers, ensuring high performance without data duplication. We implement two categories of plugins: those that modify quantities at the end of each converged self-consistent field (SCF) cycle, such as,and, and those that operate during the SCF cycle, such as. We demonstrate the utility of our implementation through three applications, structure relaxation using thelibrary, implementing an implicit solvent model, and adding thedispersion corrections. For a representative post-SCF plugin (), this coupling introduces a callback overhead of well underper ionic step relative to a natively linked implementation. This infrastructure effectively bridges the gap between high-performance electronic structure routines and the widespread scientific Python ecosystem.
PMID:
42900481
Bibliographic data and abstract were imported from PubMed on 21 Oct 2026.
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