Computational Discovery and Design of Emerging Materials: Session III
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Computational Materials Science and Engineering Committee
Program Organizers: Arunima Singh, Arizona State University; Houlong Zhuang, Arizona State University; Sugata Chowdhury, National Institute of Standards and Technology; Arun Kumar Mannodi Kanakkithodi, Purdue University
Tuesday 8:30 AM
February 25, 2020
Room: 32B
Location: San Diego Convention Ctr
Session Chair: Arun Kumar Mannodi Kanakkithodi, Argonne National Laboratory
8:30 AM Invited
First-principles Theory of Nonlinear Optical Responses in 2D Materials and Topological Materials: Xiaofeng Qian1; 1Texas A&M University
Strong nonlinear optical responses were recently observed in 2D materials and topological materials. Understanding the underlying microscopic mechanisms is important for advancing nonlinear optics. Here I will present our recent effort in developing first-principles approaches to unveil the electronic structure origin of nonlinear optical responses, including second harmonic generation and nonlinear photocurrent in noncentrosymmetric2D materials, and nonlinear ferroelectric photocurrent in topological materials. I will discuss microscopic relations between nonlinear responses and topological quantities such as shift vector, Berry curvature dipole, etc. Our findings suggest the possibility to achieve deep understanding of nonlinear optical responses in 2D materials using first-principles approaches. This research was supported by the National Science Foundation (NSF) under award number NSF DMR-1753054. References: [1] Hua Wang and Xiaofeng Qian. Nano Letters 17, 50275034 (2017). [2] Hua Wang and Xiaofeng Qian. arXiv:1811.03133 (2018). [3] Hua Wang and Xiaofeng Qian. Nonlinear Ferroelectric Photocurrent in Topological Materials, in preparation (2018).
9:00 AM
Effect of Spin-orbit Coupling on Magnetic Phase Transition of Anti-ferromagnetic Weyl-Semimetal: Sugata Chowdhury1; Kevin Garrity1; Angela Hight Walker1; Cindi Dennis1; Albert Davydov1; Francesca Tavazza1; 1National Institute of Standards and Technology
Three-dimensional materials with strong spin-orbit coupling (SOC) and broken time reversal symmetry (TRS) due to magnetic ordering have been the subject of enormous interest because, these materials can carry the spin-polarized edge states even in the absence of an external magnetic field. Our calculations reveal that the ferromagnetic phase of bulk Bi2MnTe4 (X=Se, Te) is either a nodal line or Weyl semimetal, depending on the direction of the spins and its electronic properties depend on the thickness of the materials. However, the intrinsic TRS breaking at the surface of Bi2MnTe4 removes the typical Dirac cone feature, allowing the observation of the half-integer quantum anomalous Hall effect (QAHE). We found the correlated canted spin-structure and the in-plane spin-interaction is strong compare to the out-of-plane spin-interactions. We will discuss the electron-phonon coupling and magnonic behavior of these materials. This kind of stoichiometric magnetic materials are an excellent candidate for spintronics devices.
9:20 AM
Data-driven Discovery of the Functional Form of the Superconducting Critical Temperature: Stephen Xie1; Gregory Stewart2; James Hamlin2; Peter Hirschfeld2; Richard Hennig1; 1University of Florida, Department of Materials Science and Engineering; 2University of Florida, Department of Physics
Predicting the critical temperature Tc of superconductors is a notoriously difficult task, even for electron-phonon systems. We build on earlier efforts by McMillan and Allen and Dynes to model Tc from various measures of the phonon spectrum and the electron-phonon interaction by using machine learning algorithms. Specifically, we use the Sure Independence and Sparsifying Operator (SISSO) method to identify a new, physically interpretable equation for Tc as a function of a small number of physical quantities. We show that our model, trained using the relatively small Tc < 10K data tested by Allen and Dynes, improves upon the Allen-Dynes fit and can reasonably generalize to superconducting materials with higher Tc such as H3S. By incorporating physical insights and constraints into a data-driven approach, we demonstrate that machine-learning methods can identify the relevant physical quantities and obtain predictive equations using small but high-quality datasets.
9:40 AM
High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage: Kamal Choudhary1; Kevin Garrity2; Francesca Tavazza2; 1University of Maryland(National Institute of Standards and Technology); 2Umcp/Nist
We present a novel methodology to identify topologically non-trivial materials based on band inversion induced by spin-orbit coupling (SOC) effect. Specifically, we compare the density functional theory (DFT) based wavefunctions with and without spin-orbit coupling and compute the ‘spin-orbit-spillage’ as a measure of band-inversion. Due to its ease of calculation, without any need for symmetry analysis or dense k-point interpolation, the spillage is an excellent tool for identifying topologically non-trivial materials. Out of 30000 materials available in the JARVIS-DFT database, we applied this methodology to more than 4835 non-magnetic materials consisting of heavy atoms and low bandgaps. We found 1868 candidate materials with high-spillage (using 0.5 as a threshold). We validated our methodology by carrying out conventional Wannier-interpolation calculations for 289 candidate materials. Importantly, our approach is applicable to the investigation of disordered or distorted as well as magnetic materials, because it is not based on symmetry considerations.
10:00 AM
High Throughput Exploration of Two-dimensional Topological Artificial Lattices: Srilok Srinivasan1; Mathew Cherukara1; Subramanian Sankaranarayanan1; Pierre Darancet1; 1Argonne National Laboratory
Recent progresses in nanoscale atomic manipulation and in the understanding of the topological properties of organic polymers offer new opportunities for the bottom-up synthesis of topologically-non-trivial artificial lattices, with possible use in quantum sensing and computing. With this regard, the dependence of topological properties on the shape or geometries of the nanostructures formed with these artificial lattices are largely unexplored. In this work, we employ a high throughput tight binding based framework to compute the topological invariant of the several different geometries of two-dimensional nanoribbons. A Monte Carlo based search technique is used to sample the huge design space. The topological classification is based on the quantized value of the intercell Zak phase for a given unit cell. The intercell Zak phase is obtained from the 1D Hybrid Wannier Charge centers computed by solving the tight-binding Hamiltonian.
10:20 AM Break
10:35 AM Invited
Towards a First-principles Description of Stronger Correlations: Novel Superconductors to Topological Materials: Arun Bansil1; 1Northeastern University
I will discuss with the spectacular example of the cuprate high-Tc superconductors how advanced density functionals are enabling new insights into the electronic structure, phase diagrams and magnetism of a wide variety of materials that have until now been considered to be so strongly correlated as to lie outside the scope of first-principles treatment [1-3]. I will also comment on the opportunities for a new generation of predictive modeling in correlated materials more generally, including the topological phases of quantum matter, which are drawing intense current interest [4]. Work supported by the U.S. Department of Energy. [1] J. W. Furness et al., Nature Communications Physics 1, 11 (2018). [2] C. Lane et al., Phys. Rev. B. 98, 125140 (2018). [3] Y. Zhang et al., arXiv:1809.08457.[4] A. Bansil, H. Lin and T. Das, Reviews of Modern Physics 88, 021004 (2016).
11:05 AM
Two-dimensional Functional Materials with Pentagonal Structure: Lei Liu1; Immanuella Kankam1; Houlong Zhuang1; 1Arizona State University
Single-layer pentagonal materials are an emerging family of two-dimensional (2D) materials that could exhibit novel properties. However, the electronic and magnetic properties of binary and ternary pentagonal 2D materials have been rarely studied. Here we apply density functional theory (DFT) to discover 2D functional materials with pentagonal structure. We predict 2D CoS2 through computational exfoliation method. Our DFT calculations show that CoS2 is an antiferromagnetic semiconductor. In order to increase the (super-)exchange interactions between the ions in this binary compound, we explored the ternary 2D pentagonal materials CoAsS, which lack the inversion symmetry. As expected, CoAsS exhibits higher Curie temperature of 95 K and a sizable piezoelectricity. In addition to CoAsS, we reveal 34 ternary 2D pentagonal materials, among which we focus on FeAsS. It is a semiconductor showing strong magnetocrystalline anisotropy and sizable Berry curvature. We expect more theoretical efforts to be spent towards designing 2D functional pentagonal materials.
11:25 AM
Computational Synthesis of 2D Materials: A High-throughput Approach to Materials Design: Tara Boland1; Arunima Singh1; 1Arizona State University
The emergence of two dimensional materials opened up many potential avenues for novel device applications such as nanoelectronics, topological insulators, field effect transistors, microwave and terahertz photonics and many more. To date there are over 1,000 theoretically predicted 2D materials. Only 55 2D materials have been experimentally synthesized. Computational methods such as density functional theory can be used to determine the suitable substrates to synthesize 2D materials. Using various 2D materials databases and van der Waals corrected density functional theory we investigate the suitability of 12 substrates to stabilize 2D growth. For materials which meet the criteria for suitable substrate-assisted synthesis methods such as chemical vapor deposition or mechanical exfoliation, the density of states is computed to characterize the electronic properties of these materials for device applications.
11:45 AM
Exploring Van der Waals 2D Heterostructures using a Combined Machine Learning and Density Functional Theory Approach: Daniel Willhelm1; Nathan Wilson1; Tahir Cagin1; Raymundo Arroyave1; Ruth Pachter2; Xiaofeng Qian1; 1Texas A&M University; 2Air Force Research Laboratory
Van der Waals (vdW) heterostructures consisting of vertically stacked two-dimensional (2D) materials offer an exciting opportunity for the direct tailoring of electronic structure with potential applications in ultrathin electronics and optoelectronics. In many cases, the properties of these heterostructures are unique to the individual layers, which allows for precise tuning of properties by selective stacking orders. As the number of potential 2D monolayers grows, the number of possible combinations increases dramatically. Consequently, traditional material exploration of the vast heterostructure design space is costly and time-consuming, even with the use of modern high-throughput computing. Here we present a data-driven approach to predict the properties of vdW heterostructures including bandgap energy, band alignment type, interlayer distance, etc. using thousands of unique bilayers and trilayers constructed from several 2D material families. Our approach combines machine learning methods with density functional theory which significantly reduce computational costs and speed up materials discovery and design.