Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session R56: Computational Design and Discovery of Novel Materials: Graphene and 2D MaterialsFocus Live
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Sponsoring Units: DMP DCOMP Chair: Tess Smidt, Lawrence Berkeley National Laboratory |
Thursday, March 18, 2021 8:00AM - 8:36AM Live |
R56.00001: Predicting Effective Solvents for Graphene Stabilization in Nonaqueous Dispersions Invited Speaker: Nuala Caffrey Solvents play an essential in the liquid phase exfoliation (LPE) of two-dimensional (2D) materials. The concentration of monolayers in solution after the exfoliation process depends critically on the chosen solvent [1]. The physical origin of this dependence is difficult to determine experimentally. Screening for effective solvents currently relies on matching solubility parameters between the solvent and 2D material. This makes the approximation that thermodynamic effects alone determine the nanosheet concentration. It is quite a blunt tool, with some notable failures [2]. |
Thursday, March 18, 2021 8:36AM - 8:48AM Live |
R56.00002: First-principles study of doped graphene with single and double vacancies Xiangyue Liu, Weiqi Wang, Jesús Pérez-Ríos The electronic properties of graphene can be manipulated by introducing dopant impurity. In this work, we study single-atom doped graphene in single and double vacancies from a density functional theory approach. More than 70 different dopant elements are investigated throughout the periodic table. The electronic structure and the related properties, such as bandgap and magnetic moment, are analyzed. We also discuss the influence of configuration on these properties. These results suggest the possibility of manipulating the electronic properties of graphene and broad application prospects. |
Thursday, March 18, 2021 8:48AM - 9:00AM Live |
R56.00003: Two-dimensional magnetic order in Cr-based transition metal chalcogenides Jan Phillips, Victor Pardo
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Thursday, March 18, 2021 9:00AM - 9:12AM Live |
R56.00004: Machine Learning-Enabled Design of Point Defects in 2D Materials Nathan C Frey, Deji Akinwande, Deep M Jariwala, Vivek b Shenoy Engineered point defects in two-dimensional (2D) materials offer an attractive platform for solid-state devices that exploit tailored opto-electronic, quantum emission, and resistive properties. Here, we develop an approach based on deep transfer learning, machine learning, and first-principles calculations to rapidly predict key properties of point defects. Properties including band gap and bulk formation energy are predicted for over 4,000 2D materials using deep transfer learning. 10,000 dopant, vacancy, divacancy, and antisite defect structures are generated in 150 wide band-gap materials and more than 1,000 defect band structures are computed via first-principles methods. We use physics-informed featurization to generate a minimal description of defect structures and present a general picture of defects across materials systems. We identify over 100 promising, unexplored dopant defect structures in layered metal chalcogenides, hexagonal nitrides, and metal halides including GeS, h-AlN, and MgI2. We also find ten optimal substitutional defects for nonvolatile resistive switching in atomically thin memristor devices. Au and Ag substitutions in WTe2- and MoTe2-based devices are predicted to have switching voltages as low as 110 meV. |
Thursday, March 18, 2021 9:12AM - 9:24AM Live |
R56.00005: Stability, Electronic, Magnetic and Piezoelectric Properties of Two Dimensional Transition Metal Silicates Kayahan Saritas, Nassar Doudin, Eric I. Altman, Sohrab Ismail-Beigi Two-dimensional van der Waals (2D vdW) materials that display ferromagnetism and piezoelectricity have received increased attention. Despite the recent observation of ferromagnetism in single CrI3 sheets, developing an air stable and transferable single-layer vdW material that is multiferroic has been challenging. To address this problem, we will report our work on layered transition metal silicates that are derivatives of kaolinites and lizardites with transition metal substituting on Al$^{3+}$ and Mg$^{2+}$ sites using ab-initio calculations. Using Density Functional Theory (DFT), we show that these compounds are stable under varying O$_2$ partial pressure and can be synthesized using a surface assisted method [1]. We show that these materials have finite out-of-plane piezoelectric response thanks to the lack of inversion symmetry and also they can be tailored to be ferrimagnetic with a non-zero net moment. |
Thursday, March 18, 2021 9:24AM - 9:36AM Live |
R56.00006: Piecewise Hamiltonian construction of a metal (NbS2)/semiconductor(WSe2) two-dimensional (2D) hybrid heterojunction. Poonam Kumari, Luca Sementa, Alessandro Fortunelli Density-Functional Theory studies of periodic systems can be greatly accelerated by projecting the Kohn-Sham Hamiltonian onto a limited basis set, as in the Tight-Binding (TB) and the Wannier approach to localized molecular orbitals. While the implementation of TB/Wannier approach is straightforward for systems composed of unit cells, the extension to complex, multi-component systems presents issues related to the homogeneous description of the different pieces and their interaction. Here we propose a simple protocol to decompose a system into fragments and re-build the Hamiltonian of the complete system in a piece-wise manner in terms of Hamiltonians of the separate fragments. A seamless junction between two different pieces is achieved by assuming buffer overlap regions between each pair of fragments and calculating the off-diagonal terms of the Hamiltonian by projecting the Wannier orbitals at the border of one fragment onto those of the neighboring module. We validate the method by applying it to a monolayer-thick lateral heterostructure joining two different transition metal dichalcogenides as a paradigmatic example of a metal (NbS2)/semiconductor(WSe2) hybrid heterojunction, finding a good agreement between exact and approximate Hamiltonians. |
Thursday, March 18, 2021 9:36AM - 9:48AM Live |
R56.00007: Resonant tunneling and the transmission over a non-square barrier in α-T3 lattice Nicholas Weekes, Andrii Iurov, Liubov Zhemchuzhna, Godfrey Anthony Gumbs, Danhong Huang We have derived the semiclassical WKB (Wentzel-Kramers-Brillouin) electronic states, and the |
Thursday, March 18, 2021 9:48AM - 10:00AM Live |
R56.00008: Designing a unique 2D/1D assembly for an efficient detection of the full IR spectrum Tuhin Maji, Debjani Karmakar Efficient detection of IR spectrum persists to be the centre-stage activity of opto-electronic detectors because of its application in the field of night vision, surveillance, remote sensing and thermal imaging. We have investigated the optical performance of a series of 2D/1D lateral and vertical hetero-structures, consisting of the monolayers of Graphene or MoS2 and nanowires of Te (TeNW). With the help of first-principles calculations, we predict that a detection of the full IR spectrum can be materialized with MoS2/TeNW and Graphene/TeNW lateral and vertical hetero-structures. The band-gap modulation of MoS2 due to the presence of quantum confined s-p hybridized states of Te at the MoS2/TeNW assembly materializes a potential photo-detection in the near IR range. The interaction of Te-5p and s-p hybridized states of Graphene at the interfacial contact region opens a small band gap for the Graphene/TeNW assembly and thereby renders it to be a potential system for far-IR detection. |
Thursday, March 18, 2021 10:00AM - 10:12AM Live |
R56.00009: Air-Stable Monolayer Cu2Se Exhibits a Purely Thermal Structural Phase Transition Yang Song, Kai Qian, Lei Gao, Xiya Chen, Yu-Yang Zhang, Xiao Lin, Shixuan Du, Min Ouyang, Sokrates T Pantelides, Hongjun Gao Materials possessing structural phase transformations exhibit a rich set of physical and chemical properties that can be used for various applications. However, stoichiometry-preserving, purely thermal, reversible phase transitions have not been observed. Here, we report a purely thermal structural phase transition in a new 2D material, monolayer Cu2Se by using scanning tunneling microscopy, scanning transmission electron microscopy, and density functional theory (DFT) calculations. DFT calculations trace the phase-transition mechanism via the existence/absence of imaginary (unstable) phonon modes at low and high temperatures. In addition, DFT calculations show that a degeneracy at the Γ point of the energy bands of the high-temperature phase is lifted in the low-temperature phase, confirmed by the angle resolved photoemission spectra. The variable-temperature low-energy electron diffraction patterns indicate that the phase transition occurs across the whole sample at ≈147 K. This work provides a new platform for future investigations of such phase transitions in 2D materials. [Adv. Mater. 32, 1908314, (2020)] |
Thursday, March 18, 2021 10:12AM - 10:24AM Live |
R56.00010: A machine learning approach to predict the magnetic property of metal-doped graphene Eric Inclan, Jack Lasseter, Lizhi Zhang, Mina Yoon Metal-doped graphene materials have a wide range of applications. In particular, this study seeks to predict the magnetic moment of metal-doped graphene. Although density functional theory (DFT) provides accurate predictions, it is a computationally expensive approach, and due to being a quantum mechanical modelling method, does not produce human-readable models of molecule-level properties in terms of atomic-level properties and interactions. To aid the search for such predictive models, this research implements a machine-learning approach that couples a multi-objective genetic algorithm (MOGA) to the sure independence screening and sparsifying operator (SISSO). The MOGA will begin with an initial data set of candidate variables and magnetic moments calculated using DFT, and then optimize based on model goodness of fit and parsimony. Initial results on small monovacancy graphene supercells are promising, and indicate a relationship between the magnetic moment and other key features, such as d-orbital electron configurations, dopant bond length, and dopant height out of plane, as well as higher-order nonlinearities due to band gap, and binding energy. This presentation will showcase the extension of this approach to larger supercells, as well as other related configurations. |
Thursday, March 18, 2021 10:24AM - 10:36AM Live |
R56.00011: Computational design and phase prediction of transition metal dichalcogenides using machine learning techniques Pankaj Kumar, Vinit Sharma, Pratibha Dev Two-dimensional transition metal dichalcogenides (TMDs) offer unprecedented opportunities for fundamental science and technology. These materials most commonly adopt either a trigonal prismatic phase (1H) or an octahedral symmetry phase (1T) and display diverse electronic and structural properties. Research efforts to synthesize new TMDs have led to the recent discovery of Janus TMDs, with two different chalcogens on the two faces of a monolayer. Their synthesis increases the combinatorial possibilities for synthesizing TMDs. In this work, we used high throughput calculations in combination with machine learning methods to accelerate the hunt for newer TMDs for various applications. We explored different physiochemical factors (descriptors) that govern not only the stability of TMDs but also their preferred phases. The latter has been a long-standing problem, and we show that one needs to go beyond descriptors considered within Pauling’s ratio rules. |
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