Automatic generation of input files with optimised k-point meshes for Quantum Espresso self-consistent field single point total energy calculations

TL;DR

This paper presents a machine learning method to automatically generate optimized k-point meshes for Quantum Espresso DFT calculations, reducing the need for manual convergence testing in high-throughput workflows.

Contribution

It introduces a data-driven approach to predict suitable k-point sampling parameters from material structures, enabling automated input file generation for DFT calculations.

Findings

Achieved high accuracy in predicting k-point distances within convergence thresholds.

Developed models that provide uncertainty estimates to ensure reliable predictions.

Made the best models accessible via an open web application.

Abstract

Performing density functional theory (DFT) calculations requires a careful choice of computational parameters to ensure convergence and obtain meaningful results. This represents a particularly important problem for high-throughput and agentic workflows, where due to computational cost, any additional convergence studies are preferably to be avoided. So, there is a need for tools and models which are able to predict DFT parameters from basic input information, such as a structure. In this work, we develop a machine learning approach to predict the appropriate k-point sampling in DFT calculations and generate the input files for Quantum Espresso self-consistent field calculations. To achieve this, we first generated a training dataset comprising over 20,000 materials, each with an energy convergence threshold of 1 meV/atom. Several ML models were evaluated for their ability to predict k-points distance, and uncertainty estimation was incorporated to guarantee that, for at least 85-95% of compounds, the predicted k-distance lies within the convergence region. The best-performing models are made publicly available through an open-access web application.