Journal Article FZJ-2023-04521

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How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

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2024
Springer Nature London

Nature reviews / Physics 6, 45-58 () [10.1038/s42254-023-00655-3]

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Abstract: Density-functional theory methods and codes adopting periodic boundary conditions are extensively used in condensed matter physics and materials science research. In 2016, their precision (how well properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. In this Expert Recommendation, we discuss recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z = 1 to 96 and characterizing 10 prototypical cubic compounds for each element: four unaries and six oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Finally, we discuss the extent to which the current results for total energies can be reused for different goals.

Classification:

Contributing Institute(s):
  1. Quanten-Theorie der Materialien (PGI-1)
  2. Quanten-Theorie der Materialien (IAS-1)
  3. JARA-FIT (JARA-FIT)
  4. JARA - HPC (JARA-HPC)
  5. Materials Data Science and Informatics (IAS-9)
Research Program(s):
  1. 5211 - Topological Matter (POF4-521) (POF4-521)
  2. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
  3. MaX - MAterials design at the eXascale. European Centre of Excellence in materials modelling, simulations, and design (824143) (824143)
  4. Helmholtz Platform for Research Software Engineering - Preparatory Study (HiRSE_PS-20220812) (HiRSE_PS-20220812)
  5. AIDAS - Joint Virtual Laboratory for AI, Data Analytics and Scalable Simulation (aidas_20200731) (aidas_20200731)

Appears in the scientific report 2024
Database coverage:
Medline ; OpenAccess ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; DEAL Nature ; Essential Science Indicators ; IF >= 30 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection
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The record appears in these collections:
Dokumenttypen > Aufsätze > Zeitschriftenaufsätze
JARA > JARA > JARA-JARA\-FIT
JARA > JARA > JARA-JARA\-HPC
Institutssammlungen > IAS > IAS-9
Institutssammlungen > IAS > IAS-1
Institutssammlungen > PGI > PGI-1
Workflowsammlungen > Öffentliche Einträge
Publikationsdatenbank
Open Access

 Datensatz erzeugt am 2023-11-15, letzte Änderung am 2025-04-01


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