Kevin Parrish

Bio: Kevin Parrish is an academic researcher from University of Nevada, Las Vegas. The author has contributed to research in topics: Wyckoff positions & Energy minimization. The author has an hindex of 2, co-authored 2 publications receiving 16 citations.

Switchable two-dimensional electrides: A first-principles study

Abstract: Electrides, with excess anionic electrons confined in their empty space, are promising for uses in catalysis, nonlinear optics, and spin electronics. However, the application of electrides is limited by their high chemical reactivity. In this paper, we report a group of chemically stable monolayer electrides with the presence of switchable nearly free electron (NFE) states in their electronic structures. Unlike conventional electrides, which are metals with floating electrons forming the bands crossing the Fermi level, the switchable electrides are semiconductors holding the NFE states close to the Fermi level. According to a high-throughput search, we identified 11 candidate materials with low-energy NFE states that can likely be exfoliated from the known layered materials. Under external strain, these NFE states, stemming from the surface image potential, will be pushed downward to cross the Fermi level. The critical semiconductor-metal transition can be achieved by a strain within 10% in several monolayer materials. These switchable electrides may provide an ideal platform for exploring quantum phenomena and modern electronic device applications.

crystIT: complexity and configurational entropy of crystal structures via information theory

Abstract: The information content of a crystal structure as conceived by information theory has recently proved an intriguing approach to calculate the complexity of a crystal structure within a consistent concept. Given the relatively young nature of the field, theory development is still at the core of ongoing research efforts. This work provides an update to the current theory, enabling the complexity analysis of crystal structures with partial occupancies as frequently found in disordered systems. To encourage wider application and further theory development, the updated formulas are incorporated into crystIT (crystal structure and information theory), an open-source Python-based program that allows for calculating various complexity measures of crystal structures based on a standardized *.cif file.

Demonstrating the Potential of Alkali Metal-Doped Cyclic C6O6Li6 Organometallics as Electrides and High-Performance NLO Materials.

Abstract: In this report, the geometric and electronic properties and static and dynamic hyperpolarizabilities of alkali metal-doped C6O6Li6 organometallics are analyzed via density functional theory methods. The thermal stability of the considered complexes is examined through interaction energy (Eint) calculations. Doping of alkali metal derives diffuse excess electrons, which generate the electride characteristics in the respective systems (electrons@complexant, e-@M@C6O6Li6, M = Li, Na, and K). The electronic density shifting is also supported by natural bond orbital charge analysis. These electrides are further investigated for their nonlinear optical (NLO) responses through static and dynamic hyperpolarizability analyses. The potassium-doped C6O6Li6 (K@C6O6Li6) complex has high values of second- (βtot = 2.9 × 105 au) and third-order NLO responses (γtot = 1.6 × 108 au) along with a high refractive index at 1064 nm, indicating that the NLO response of the corresponding complex increases at a higher wavelength. UV-vis absorption analysis is used to confirm the electronic excitations, which occur from the metal toward C6O6Li6. We assume that these newly designed organometallic electrides can be used in optical and optoelectronic fields for achieving better second-harmonic-generation-based NLO materials.

Electron-Deficient-Type Electride Ca 5 Pb 3: Extension of Electride Chemical Space

Abstract: Electrides have been identified so far by two major routes: one is conversion of elemental metals and stoichiometric compounds by high pressure; the other is to search for electron-rich compounds, and this approach is more general. In contrast, few electron-deficient structures in existing databases have been revealed as potential electride candidates. In this work, we found an electron-deficient compound Ca5Pb3 could be transformed into electrides upon applying external pressure or strain along the c-axis, which induces the electron immigration from Pb to interstitial sites. Furthermore, the electron doping via Hf substitution of Ca atoms for Ca5Pb3 was found to be capable of tuning the interstitial electron density under ambient pressure, resulting in a new stable ternary electride Ca3Hf2Pb3, Hf-substituted Ca5Pb3. The electron-deficient electride discovered here is of novel type and can largely expand the research scope of electrides. Considering a recently reported neutral electride Na3N and the present finding, it is now clarified that electrides can be identified irrespective of stoichiometry (electron-rich, -neutral, or -poor) for compounds.

Neural network potential from bispectrum components: A case study on crystalline silicon.

Abstract: In this article, we present a systematic study on developing machine learning force fields (MLFFs) for crystalline silicon. While the main-stream approach of fitting a MLFF is to use a small and localized training set from molecular dynamics simulations, it is unlikely to cover the global features of the potential energy surface. To remedy this issue, we used randomly generated symmetrical crystal structures to train a more general Si-MLFF. Furthermore, we performed substantial benchmarks among different choices of material descriptors and regression techniques on two different sets of silicon data. Our results show that neural network potential fitting with bispectrum coefficients as descriptors is a feasible method for obtaining accurate and transferable MLFFs.