A bulk adjusted linear combination of atomic orbitals (BA-LCAO) approach for nanoparticles

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Abstract

We describe a bulk adjusted linear combination of atomic orbitals (BA-LCAO) approach for nanoparticles. In this method, we apply a many-body scaling function (in similar manner as in the environment-modified total energy based tight-binding method) to the DFT-derived diatomic AO interaction potentials (like in the conventional orbital-based density-functional tight binding approach) strictly according to atomic valences acquired naturally in a bulk structure. This modification, (a) facilitates all atom orbital-based electronic structure calculations of charge carrier dynamics in nanoscale structures with a molecular acceptor, and (b) allows to closely match high-level density functional calculation data (previously adjusted to the available experimental findings) for bulk structures. To advance practical application of the BA-LCAO approach we parameterize the Hamiltonian of wurtzite CdSe by fitting its band structure to a high-level DFT reference, corrected for experimentally measured band edges. Here, unlike in conventional DFTB approach, we: (1) use hydrogen-like AOs for the basis as exact atomic eigenfunctions, while orbital energies of which are taken from experimentally measured ionization potentials, and (2) parameterize the many-body scaling functions rather than the atomic wavefunctions. Development of this approach and parameters is guided by our goals to devise a method capable of simultaneously treating the problems of (i) interfacial electron/hole transfer between finite, variable size nanoparticles and electron scavenging molecules, and (ii) high-energy electronic transitions (Auger transitions) that mediate multi-exciton decay in quantum dots. Electronic structure results are described for CdSe quantum dots of various sizes. © 2018 Wiley Periodicals, Inc.

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Kaledin, A. L., Hill, C. L., Lian, T., & Musaev, D. G. (2019). A bulk adjusted linear combination of atomic orbitals (BA-LCAO) approach for nanoparticles. Journal of Computational Chemistry, 40(1), 212–221. https://doi.org/10.1002/jcc.25373

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