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

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. Verification efforts of density-functional theory (DFT) calculations are of crucial importance to evaluate the reliability of simulation results. In this Expert Recommendation, we suggest metrics for DFT verification, illustrating them with an all-electron reference dataset of 960 equations of state covering the whole periodic table (hydrogen to curium) and discuss the importance of improving pseudopotential codes. Verification efforts are critical to assess the reliability of density-functional theory (DFT) simulations and provide results with properly quantified uncertainties. Developing standard computation protocols to perform verification studies and publishing curated and FAIR reference datasets can greatly aid their use to improve codes and computational approaches. The use of fully automated workflows with common interfaces between codes can guarantee uniformity, transferability and reproducibility of results. A careful description of the numerical and methodological details needed to compare with the reference datasets is essential; we discuss and illustrate this point with a dataset of 960 all-electron equations of state. Reference datasets should always include an explanation of the target property for which they were generated, and a discussion of their limits of applicability. Further extensions of DFT verification efforts are needed to cover more functionals, more computational approaches and the treatment of magnetic and relativistic (spin–orbit) effects. They should also aim at concurrently delivering optimized protocols that not only target ultimate precision, but also optimize the computational cost for a target accuracy. Verification efforts are critical to assess the reliability of density-functional theory (DFT) simulations and provide results with properly quantified uncertainties. Developing standard computation protocols to perform verification studies and publishing curated and FAIR reference datasets can greatly aid their use to improve codes and computational approaches. The use of fully automated workflows with common interfaces between codes can guarantee uniformity, transferability and reproducibility of results. A careful description of the numerical and methodological details needed to compare with the reference datasets is essential; we discuss and illustrate this point with a dataset of 960 all-electron equations of state. Reference datasets should always include an explanation of the target property for which they were generated, and a discussion of their limits of applicability. Further extensions of DFT verification efforts are needed to cover more functionals, more computational approaches and the treatment of magnetic and relativistic (spin–orbit) effects. They should also aim at concurrently delivering optimized protocols that not only target ultimate precision, but also optimize the computational cost for a target accuracy.