密度泛函理论
赝势
计算机科学
周期边界条件
可靠性(半导体)
航程(航空)
工作流程
周期表
理论计算机科学
计算科学
边值问题
计算化学
化学
物理
材料科学
量子力学
数据库
功率(物理)
复合材料
作者
Emanuele Bosoni,Louis Beal,Marnik Bercx,Peter Blaha,Stefan Blügel,J. D. Broder,Martin Callsen,Stefaan Cottenier,Augustin Degomme,Vladimir Dikan,Kristjan Eimre,Espen Flage−Larsen,Marco Fornari,Alberto Garcı́a,Luigi Genovese,Matteo Giantomassi,Sebastiaan P. Huber,Henning Janssen,Georg Kastlunger,Matthias Krack
标识
DOI:10.1038/s42254-023-00655-3
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI