Accurate and efficient protocols for high-throughput first-principles materials simulations

Abstract Advancements in theoretical and algorithmic approaches, workflow engines, and an ever-increasing computational power have enabled a novel paradigm for materials discovery through first-principles high-throughput simulations. A major challenge in these efforts is to automate the selection of parameters used by simulation codes to deliver numerical precision and computational efficiency. Here, we propose a rigorous methodology to assess the quality of self-consistent DFT calculations with respect to smearing and k -point sampling across a wide range of crystalline materials. For this goal, we develop criteria to reliably estimate average errors on total energies, forces, and other properties as a function of the desired computational efficiency, while consistently controlling k -point sampling errors. The present results provide automated protocols (named standard solid-state protocols or SSSPr) for selecting optimized parameters based on different choices of precision and efficiency tradeoffs. These are available through open-source tools that range from interactive input generators for DFT codes to high-throughput workflows.

Authors

Publication Details

Journal
npj Computational Materials
Published
2026-04-29
DOI
https://doi.org/10.1038/s41524-026-02097-8
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accurate and efficient protocols for high-throughput first-principles materials simulations

Flaviano José dos Santos, Davide Grassano, Giovanni Pizzi, Nicola Marzari et al.
npj Computational Materials
Machine Learning in Materials Science
article

Accurate and efficient protocols for high-throughput first-principles materials simulations

Flaviano José dos Santos, Davide Grassano, Giovanni Pizzi, Nicola Marzari, Marnik Bercx, Gabriel de Miranda Nascimento
article en

Abstract

Abstract Advancements in theoretical and algorithmic approaches, workflow engines, and an ever-increasing computational power have enabled a novel paradigm for materials discovery through first-principles high-throughput simulations. A major challenge in these efforts is to automate the selection of parameters used by simulation codes to deliver numerical precision and computational efficiency. Here, we propose a rigorous methodology to assess the quality of self-consistent DFT calculations with respect to smearing and k -point sampling across a wide range of crystalline materials. For this goal, we develop criteria to reliably estimate average errors on total energies, forces, and other properties as a function of the desired computational efficiency, while consistently controlling k -point sampling errors. The present results provide automated protocols (named standard solid-state protocols or SSSPr) for selecting optimized parameters based on different choices of precision and efficiency tradeoffs. These are available through open-source tools that range from interactive input generators for DFT codes to high-throughput workflows.

npj Computational Materials
Openalex Percentile: Top 13%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.