# ELSI: A Unified Software Interface for Kohn-Sham Electronic Structure Solvers

## TL;DR

ELSI is a unified software interface that simplifies access to multiple algorithms for solving Kohn-Sham eigenproblems in electronic structure calculations, improving efficiency and flexibility for large-scale density-functional theory simulations.

## Contribution

It introduces a unified framework that integrates various eigenproblem solvers, with default parameters and automatic format conversions, facilitating easier implementation and optimization in electronic structure codes.

## Key findings

- Benchmarks up to 11,520 atoms demonstrate scalability.
- ELSI improves solver flexibility and usability.
- Automatic solver recommendation enhances performance.

## Abstract

Solving the electronic structure from a generalized or standard eigenproblem is often the bottleneck in large scale calculations based on Kohn-Sham density-functional theory. This problem must be addressed by essentially all current electronic structure codes, based on similar matrix expressions, and by high-performance computation. We here present a unified software interface, ELSI, to access different strategies that address the Kohn-Sham eigenvalue problem. Currently supported algorithms include the dense generalized eigensolver library ELPA, the orbital minimization method implemented in libOMM, and the pole expansion and selected inversion (PEXSI) approach with lower computational complexity for semilocal density functionals. The ELSI interface aims to simplify the implementation and optimal use of the different strategies, by offering (a) a unified software framework designed for the electronic structure solvers in Kohn-Sham density-functional theory; (b) reasonable default parameters for a chosen solver; (c) automatic conversion between input and internal working matrix formats, and in the future (d) recommendation of the optimal solver depending on the specific problem. Comparative benchmarks are shown for system sizes up to 11,520 atoms (172,800 basis functions) on distributed memory supercomputing architectures.