Stability and electronic properties of oxygen-doped ZnS polytypes: DFTB study

Popov, I. S.; Ворох, А. С.; Enyashin, Andrey N.

doi:10.1016/j.chemphys.2018.04.017

Cited by 6 publications

(2 citation statements)

References 41 publications

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“…It offers an alternative to DFT, providing relatively high accuracy at low computational cost and is well-suited for extended systems with large unit cells. DFTB has already found numerous successes in a wide range of systems, including organic and biomolecules, organic–inorganic hybrid materials, metals, − semiconductors, − and so forth, and in solving various problems, for example, electron transport, − optical, and magnetic properties. , To further increase the accuracy of the DFTB method, the Hamiltonian ( H ) and overlap ( S ) matrices of the DFTB method can be optimized.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enhanced DFTB Method for Periodic Systems: Learning from Electronic Density of States

Sun

Fan

Heide

et al. 2023

J. Chem. Theory Comput.

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Density functional tight binding (DFTB) is an approximate density functional based quantum chemical simulation method with low computational cost. In order to increase its accuracy, we have introduced a machine learning algorithm to optimize several parameters of the DFTB method, concentrating on solids with defects. The backpropagation algorithm was used to reduce the error between DFTB and DFT results with respect to the training data set and to obtain adjusted DFTB Hamiltonian and overlap matrix elements. Afterward, the generalization capability of the trained model was tested for geometries not being part of the training set. In the current work, we have focused on defective periodic silicon and silicon carbide systems as target materials and the density of states (DOS) as target property to demonstrate the feasibility of our approach. The trained model was able to reduce the differences between the DFTB and DFT DOS significantly, while other derived properties (for example, Mulliken population distribution, projected DOS) remained physically sound. Also, the transferability of the obtained model could be verified. Our method allows to carry out relatively fast simulations with high accuracy and only moderate training efforts, and represents a good compromise for cases, where long-range effects make direct machine learning predictions difficult.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Enhanced DFTB Method for Periodic Systems: Learning from Electronic Density of States

Sun

Fan

Heide

et al. 2023

J. Chem. Theory Comput.

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“…We have previously demonstrated that the O impurity replacing the S atoms has no significant effect on the mutual thermodynamic stability of the wurtzite and sphalerite phases, as well as of their mixed polytypes [27]. Here, the quantum chemistry method is employed to perform a comparative analysis of the chemical form, localization, and energy difference of the N impurity hosted at sphalerite and wurtzite lattices.…”

Section: Introductionmentioning

confidence: 99%

Effect of nitrogen impurities on ZnS polymorphism

Popov¹,

Ворох²,

Enyashin³

2019

Nanosystems: Phys. Chem. Math.

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The ZnS polymorphs-sphalerite and wurtzite-have the very close formation energies, setting their coexistence in nature. Moreover, numerous cases of a disordered phase formation based on these polymorphs have been registered. However, sphalerite is a common mineral, while wurtzite is rare. Perhaps the wider distribution of sphalerite can be explained by means of stabilizing effect from impurities. In this paper, the most stable form and the localization of nitrogen impurities in both ZnS polymorphs is screened using the methods of quantum chemistry. The influence of impurity on polymorphic wurtzite-sphalerite equilibrium is disclosed. According to the obtained results, the introduction of nitrogen impurities facilitates the domination of sphalerite over wurtzite.

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