Bulletin of the American Physical Society
Mid-Atlantic Section 2022 Meeting
Volume 67, Number 20
Friday–Sunday, December 2–4, 2022; University Park, PA, Pennsylvania State University
Session G03: Quantum Materials and Nanostructures II |
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Chair: Wilson Yanez, Pennsylvania State University Room: Pennsylvania State University Osmond 105 |
Sunday, December 4, 2022 9:00AM - 9:35AM |
G03.00001: Machine Learning-guided Design of Emerging 2D-based Materials Invited Speaker: Srihari M Kastuar A considerable number of novel computational and experimental approaches have emerged in response to the growing interest in two-dimensional (2D) materials. Machine Learning (ML) techniques have become increasingly essential in recent years, with numerous ML-based approaches being employed to investigate diverse classes of materials for specific applications. In this talk, we will highlight two of our recent initiatives to use materials informatics and machine learning in the design of next-generation 2D materials. We developed an ML algorithm to efficiently guide the selection of the lattice parameters for both atomically thin 2D materials and associated heterostructures, with an out-of-sample accuracy as high as 90%. Due to their unique and tunable optoelectronic properties, 2D/organic hybrid materials are intriguing quantum materials, but the development of computational tools and screening of the database of millions of such possible materials remains a challenging task. In the second part of this talk, will present our recent efforts in developing an ML-guided pipeline, materials screening, design, and first-principles calculations of complex quantum materials derived from 2D-based transition metal dichalcogenides and organic molecules via electrochemical intercalation. Using the bootstrap approach, we will show how to efficiently pick interesting materials for future computational and experimental examination based on parameters such as intercalation energy. |
Sunday, December 4, 2022 9:35AM - 10:10AM |
G03.00002: Understanding the field-polarized state that hosts incredibly high-field superconductivity in uranium ditelluride Invited Speaker: Sylvia K Lewin Uranium ditelluride (UTe2) was recently discovered to be a superconductor with a transition temperature of approximately 2 K. Its superconducting state is quite exotic and all evidence to date suggests that it is a spin-triplet superconductor, one of only a few ever discovered. This makes UTe2 interesting both on the basis of fundamental physics and for its potential use in topological qubits that would enable fault-tolerant quantum computing. |
Sunday, December 4, 2022 10:10AM - 10:22AM |
G03.00003: Plasmonic resonance shift of metallic nanostructures studied in four different ways Jiantao Kong For simple metals such as sodium, the surface plasmon dispersion relation in the long wavelength limit of planar geometry [1] and the size dependence of plasmonic resonance shift of spherical geometry [2], are largely due to the inhomogeneous spilled-out electron density profile at the surface. We propose a semi-classical computation method based on earlier studies, to recover the quantum effects within classical framework [3] and produce the resonance shifts exact to first order. We benchmark the accuracy and efficiency of the proposed method against results from ab initio time dependent density functional theory (TDDFT) [4]. |
Sunday, December 4, 2022 10:22AM - 10:34AM |
G03.00004: Controllable Generation and Modulation of Antisite Defects in MoS2 and WS2 Burcu OZDEN It is critical to understand the laws of quantum mechanics in transformative new technologies for computation and quantum information science applications to enable the ongoing second quantum revolution calls. Recently, spin qubits based on point defects have gained great attention since these qubits can be initiated, selectively controlled, and read out with high precision at ambient temperatures. Qubits are important in the quantum field because they improve the security of data stored and allow for faster message delivery and encrypted networks for security-related data. The major challenge in these systems is controllably generating multiqubit systems while also properly coupling the defects. To address this issue, we propose beginning by tackling the engineering challenges these systems present and understanding the fundamentals of defects. With this regard, MoS2 and WS2 are superior platforms for realizing controlled creation and manipulation of defects due to its properties of being atomically thin and receptive to external controls.In this research, we identify a technique for generating and controlling the antisite defects to open a new pathway for creating scalable, room temperature spin qubits in 2D materials.. We quantitively discovered that both the density and the nature of defects can be modulated by the proton energy; high defect density was observed with lower proton irradiation energies. Three distinct defect types of vacancies, antisites, and adatoms were observed. In particular, creation and manipulation of antisite defects provide an alternative way to create and pattern spin qubits based on point defects. Our results demonstrate that the formation of defects can be controlled using various particle irradiation energies, leading to new opportunities for tuning the properties of 2D materials and fabricate reliable devices |
Sunday, December 4, 2022 10:34AM - 10:46AM |
G03.00005: Investigating fluorination of thin metal oxides through spin coating and vapor transport treatments Ryan S Paxson, Benjamin A Moore, Joseph Kromer, Taylor Pettaway, Victor Terranova, Richard Seabrease, David Schaefer, Vera Smolyaninova, Rajeswari M Kolagani We have studied the effects of two ‘fluorination’ treatments, spin coating and vapor transport, on the structural, magnetic, and electrical properties of La0.67 Ca0.33MnO 3-y (LCMO) thin films. Spin coating fluorination treatment involves coating the films with a fluorine containing polymer Poly-Vinyl Difluoride (PVDF) followed by an ex-situ heat treatment. Spin coating fluorination treatment of oxygen deficient films decreases the resistivity by several orders of magnitude, compared to as grown. and induces the insulator-metal transition. X-ray diffraction shows an additional phase which corresponds to a shortened c-lattice parameter. Identical control samples subject to the same thermal treatment without the PVDF coating do not show any significant changes.The drastic decrease in resistivity and the occurrence of the insulator-to-metal transition indicate that incorporation of fluorine at oxygen vacancy sites may lead to increase in the hole doping thus promoting a higher Mn4+ to Mn3+ ratio. Vapor transport fluorination (VTF) involves heating PVDF pellets and in a tube furnace under flowing argon gas. Similar experiments on another perovskite material (SrFeO3) reported in literature shows that VTF process lengthens the c-lattice parameter of thin film samples as compared to as grown films, the opposite result of the spin coating treatment, suggesting that VTF is reductive. We will present our results comparing the effects of fluorination on LCMO thin films by spin coating and vapor transport. |
Sunday, December 4, 2022 10:46AM - 10:58AM |
G03.00006: Neural Network Potentials for Nonstoichiometric Materials: a case study for chromium sulfides Akram Ibrahim, Daniel Wines, Can Ataca Controlling the properties of nonstoichiometric semiconductors through modifying their structural vacancy compositions can lead to a plethora of applications in many disciplines of materials science. These materials can give rise to a lot of stable phases over a wide range of compositions, where the crystal structure is often unknown. The large size of the composition space and the associated structure space that need to be explored for crystal structure prediction makes first-principles methods ill-suited for this task, especially for compositions that require large numbers of atoms per unit cell. We here fit a neural network potential (NNP), trained on data from density functional theory (DFT), to generalize to the special quasi-random structures (SQS) of nonstoichiometric chromium sulfides, Cr(1−x)S, over the full range of Cr vacancy concentrations. We indicate that NNP is able to accurately rank the SQS cells available at each composition and identify the ground-state structures, providing a superior performance compared to the conventional cluster expansion Hamiltonian. Furthermore, we show the ability of NNP to reproduce the DFT properties for the identified ground-state structures such as the vibrational properties, phonon dispersion relations, and equations of state. Finally, we use NNP to predict the exfoliation energy of multilayered Cr(1−x)S slabs as a function of thickness and composition, to indicate the capability of exfoliating nanosheets from the bulk phases. |
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