2015
DOI: 10.1016/j.cpc.2015.06.014
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Automated generation of highly accurate, efficient and transferable pseudopotentials

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Cited by 12 publications
(6 citation statements)
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References 23 publications
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“…In this paper, electronic cross-correlation was described with a GGA-PBE , functional theory in the CASTEP program . Using ultrasoft pseudopotential (USPP) to illustrate the interaction between ion real and valence electrons, Broyden–Fletcher–Goldfarb–Shanno (BFGS) served as the geometric optimization . Cutoff energy was set as 450 eV.…”
Section: Methodsmentioning
confidence: 99%
“…In this paper, electronic cross-correlation was described with a GGA-PBE , functional theory in the CASTEP program . Using ultrasoft pseudopotential (USPP) to illustrate the interaction between ion real and valence electrons, Broyden–Fletcher–Goldfarb–Shanno (BFGS) served as the geometric optimization . Cutoff energy was set as 450 eV.…”
Section: Methodsmentioning
confidence: 99%
“…As DAKOTA was intended to be used together with a separate application, it considers the simulation code as a ''black box''. There exist many cases in literature where DAKOTA has been coupled with different simulation software for this purpose [37][38][39]. OptiSMOKE++ was written in C++ which allows for a direct and fast communication between DAKOTA and OpenSMOKE++.…”
Section: Dakota (Design Analysis Kit For Optimization and Terascalementioning
confidence: 99%
“…The design of transferable NCPPs is essential for accurate DFT calculations. Traditionally, pseudopotential design has involved hand-picking parameters; however, optimization algorithms have recently been used to automate the selection of parameters for nonlocal pseudopotentials [9][10][11][12] . In this paper we revisit the designed-nonlocal (DNL) pseudopotential approach, in which the local potential of a separable norm-conserving pseudopotential is modified by adding an augmentation function in order to improve transferability 13 .…”
Section: Introductionmentioning
confidence: 99%
“…We consider augmentation functions in the form of a sum of cosine functions, and use a conjugate gradient algorithm to select the coefficients in the sum that minimize transferability errors for the isolated atom. Some recent work on the use of optimization algorithms to design pseudopotentials has involved selecting parameters (such as core radii and kinetic energy truncation cutoff) that optimize the accuracy of solid-state properties [9][10][11][12] . Here, we take the different approach of optimizing transferability in the isolated atom only, and we show that this leads to dramatic improvements in the accuracy of solid-state properties.…”
Section: Introductionmentioning
confidence: 99%