Auxiliary Density Functional Theory: From Molecules to Nanostructures

Calaminici, Patrizia; Alvarez-Ibarra, Aurélio; Cruz-Olvera, Domingo; Domı́nguez-Soria, Victor-Daniel; Flores‐Moreno, Roberto; Gamboa, Gabriel U.; Geudtner, Gerald; Goursot, Annick; Mejı́a-Rodrı́guez, Daniel; Salahub, Dennis R.; Zúñiga-Gutiérrez, Bernardo; Köster, Andreas M.

doi:10.1007/978-3-319-27282-5_16

Cited by 10 publications

(6 citation statements)

References 236 publications

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“…Auxiliary density functional theory (ADFT) 50,51 as implemented in the deMon2k software was employed to perform the first-principles calculations. In ADFT an approximate density is employed for the variational fitting of the Coulomb potential 52 to reduce the formal N 4 scaling of the electron repulsion integrals.…”

Section: Computational Detailsmentioning

confidence: 99%

Active‐learning for global optimization of Ni‐Ceria nanoparticles: The case of Ce_4−xNi_xO₈₋_x (x = 1, 2, 3)

Barrios Herrera,

Lourenço,

Hostaš

et al. 2024

J Comput Chem

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Ni‐CeO2 nanoparticles (NPs) are promising nanocatalysts for water splitting and water gas shift reactions due to the ability of ceria to temporarily donate oxygen to the catalytic reaction and accept oxygen after the reaction is completed. Therefore, elucidating how different properties of the Ni‐Ceria NPs relate to the activity and selectivity of the catalytic reaction, is of crucial importance for the development of novel catalysts. In this work the active learning (AL) method based on machine learning regression and its uncertainty is used for the global optimization of Ce(4‐x)NixO(8‐x) (x = 1, 2, 3) nanoparticles, employing density functional theory calculations. Additionally, further investigation of the NPs by mass‐scaled parallel‐tempering Born‐Oppenheimer molecular dynamics resulted in the same putative global minimum structures found by AL, demonstrating the robustness of our AL search to learn from small datasets and assist in the global optimization of complex electronic structure systems.

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Section: Computational Detailsmentioning

confidence: 99%

Active‐learning for global optimization of Ni‐Ceria nanoparticles: The case of Ce_4−xNi_xO₈₋_x (x = 1, 2, 3)

Barrios Herrera,

Lourenço,

Hostaš

et al. 2024

J Comput Chem

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“…Auxiliary density functional theory 58 (ADFT) ground state calculations were performed on the two model clusters, Cu 20 H 11 (S 2 PH 2 ) 9 33 and Cu 32 H 20 (S 2 PH 2 ) 12 , 52 employing the B88 exchange 59 and P86 correlation 60 functionals. The non-interacting Kohn–Sham ADFT wave function was expanded with the linear combination of Gaussian type orbital (LCGTO) approximation.…”

Section: Computational Detailsmentioning

confidence: 99%

¹H NMR global diatropicity in copper hydride complexes

et al. 2022

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“…The solution of the linear system in eq 31 , which is also required here, scales as ( N aux ) 3 but with a very small prefactor and, as we will show for a large set of molecular systems of increasing size, its actual contribution to the total elapsed time remains negligible (see Section 4 ). We mention that the scheme applied here to the evaluation of the FDE potential contribution is fully consistent with the auxiliary density functional theory 101 and it can be efficiently employed, without significant modifications, in the nonrelativistic DFT implementations. Furthermore, in our specific case, an important aspect for the computational efficiency arises from the use of an auxiliary fitting basis set of primitive HGTFs.…”

Section: Theorymentioning

confidence: 99%

Frozen-Density Embedding for Including Environmental Effects in the Dirac-Kohn–Sham Theory: An Implementation Based on Density Fitting and Prototyping Techniques

Santis

Sorbelli

Vallet

et al. 2022

J. Chem. Theory Comput.

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Frozen density embedding (FDE) represents an embedding scheme in which environmental effects are included from first-principles calculations by considering the surrounding system explicitly by means of its electron density. In the present paper, we extend the full four-component relativistic Dirac–Kohn–Sham (DKS) method, as implemented in the BERTHA code, to include environmental and confinement effects with the FDE scheme (DKS-in-DFT FDE). The implementation, based on the auxiliary density fitting techniques, has been enormously facilitated by BERTHA’s python API (PyBERTHA), which facilitates the interoperability with other FDE implementations available through the PyADF framework. The accuracy and numerical stability of this new implementation, also using different auxiliary fitting basis sets, has been demonstrated on the simple NH3–H2O system, in comparison with a reference nonrelativistic implementation. The computational performance has been evaluated on a series of gold clusters (Au n , with n = 2, 4, 8) embedded into an increasing number of water molecules (5, 10, 20, 40, and 80 water molecules). We found that the procedure scales approximately linearly both with the size of the frozen surrounding environment (consistent with the underpinnings of the FDE approach) and with the size of the active system (in line with the use of density fitting). Finally, we applied the code to a series of heavy (Rn) and super-heavy elements (Cn, Fl, Og) embedded in a C60 cage to explore the confinement effect induced by C60 on their electronic structure. We compare the results from our simulations, with respect to more-approximate models employed in the atomic physics literature. Our results indicate that the specific interactions described by FDE are able to improve upon the cruder approximations currently employed, and, thus, they provide a basis from which to generate more-realistic radial potentials for confined atoms.

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Auxiliary Density Functional Theory: From Molecules to Nanostructures

Cited by 10 publications

References 236 publications

Active‐learning for global optimization of Ni‐Ceria nanoparticles: The case of Ce_4−xNi_xO₈₋_x (x = 1, 2, 3)

Active‐learning for global optimization of Ni‐Ceria nanoparticles: The case of Ce_4−xNi_xO₈₋_x (x = 1, 2, 3)

¹H NMR global diatropicity in copper hydride complexes

Frozen-Density Embedding for Including Environmental Effects in the Dirac-Kohn–Sham Theory: An Implementation Based on Density Fitting and Prototyping Techniques

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Auxiliary Density Functional Theory: From Molecules to Nanostructures

Cited by 10 publications

References 236 publications

Active‐learning for global optimization of Ni‐Ceria nanoparticles: The case of Ce4−xNixO8−x (x = 1, 2, 3)

Active‐learning for global optimization of Ni‐Ceria nanoparticles: The case of Ce4−xNixO8−x (x = 1, 2, 3)

1H NMR global diatropicity in copper hydride complexes

Frozen-Density Embedding for Including Environmental Effects in the Dirac-Kohn–Sham Theory: An Implementation Based on Density Fitting and Prototyping Techniques

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Product

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Active‐learning for global optimization of Ni‐Ceria nanoparticles: The case of Ce_4−xNi_xO₈₋_x (x = 1, 2, 3)

Active‐learning for global optimization of Ni‐Ceria nanoparticles: The case of Ce_4−xNi_xO₈₋_x (x = 1, 2, 3)

¹H NMR global diatropicity in copper hydride complexes