Density-functional expansion methods: grand challenges

  • Timothy J. Giese
  • Darrin M. YorkEmail author
Regular Article
Part of the following topical collections:
  1. From Quantum Mechanics to Force Fields Collection


We discuss the source of errors in semiempirical density-functional expansion (VE) methods. In particular, we show that VE methods are capable of well reproducing their standard Kohn-Sham density-functional method counterparts, but suffer from large errors upon using one or more of these approximations: the limited size of the atomic orbital basis, the Slater monopole auxiliary basis description of the response density, and the one- and two-body treatment of the core-Hamiltonian matrix elements. In the process of discussing these approximations and highlighting their symptoms, we introduce a new model that supplements the second-order density-functional tight-binding model with a self-consistent charge-dependent chemical potential equalization correction; we review our recently reported method for generalizing the auxiliary basis description of the atomic orbital response density; and we decompose the first-order potential into a summation of additive atomic components and many-body corrections, and from this examination, we provide new insights and preliminary results that motivate and inspire new approximate treatments of the core-Hamiltonian.


Tight-binding models Density-functional theory Electronic structure 



The authors are grateful for financial support provided by the National Institutes of Health (GM084149). Computational resources from the Minnesota Supercomputing Institute for Advanced Computational Research (MSI) were utilized in this work. This research was supported in part by the National Science Foundation through TeraGrid resources provided by the National Center for Supercomputing Applications and the Texas Advanced Computing Center under grant TG-CHE100072.


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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Chemistry and Chemical Biology and BioMaPS Institute for Quantitative BiologyRutgers UniversityPiscatawayUSA

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