Computational Discovery and Design of Emerging Materials: Session V
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

Thursday 8:30 AM
February 27, 2020
Room: 32B
Location: San Diego Convention Ctr

Session Chair: Houlong Zhuang, Arizona State University


8:30 AM  Invited
Design of Metastable Materials: Heterostructural Alloys and Novel Nitrides: Stephan Lany1; 1National Renewable Energy Laboratory
    Materials discovery is increasingly going beyond the prediction of thermodynamically stable materials at Daltonian compositions and their crystallographic primitive cells, including, e.g., metastable compounds, solid solutions, as well as defect- and disorder-enabled materials. Following a broad search for ternary nitrides spaces, we identified novel wurtzite and rock-salt nitrides by first principles crystal structure prediction, including Zn-M (M = Sb, Mo, W) and Mg-M (M = Nb, Ti, Zr, Hf). The effects of disorder on structure selection and electronic structure properties are studied by Monte-Carlo sampling. Disorder is also the inherent driving force for the formation of solid solutions. Studying MnO-ZnO and SnS-CaS systems, we showed that heterostructural alloys can exhibit a new phase diagram behavior with markedly increased ranges of metastability, enabling the design of novel homogeneous single-phase alloys. References: 10.1021/jacs.7b12861; 10.1039/c9mh00369j; 10.1038/s41563-019-0396-2; 10.1126/sciadv.1700270

9:00 AM  
From Pentagonal Geometries to Two-dimensional Materials: Houlong Zhuang1; Lei Liu1; Duo Wang1; 1Arizona State University
    Two-dimensional (2D) materials hold a great potential as critical components of future generations of energy-efficient electronic devices such as quantum computers. Hexagons are the dominant building blocks adopted by nearly all of the promising 2D materials such as single-layer molybdenum disulphide and chromium triiodide exhibiting exotic electrical and magnetic properties, respectively. We have recently spent efforts in discovering and designing 2D materials based on pentagonal geometries possessing intrinsic anisotropy. From the tessellation point of view, there are 15 types of convex pentagons discovered so far capable of tiling a gapless plane. Namely, any of these 15 types of pentagonal can be adopted as the crystal structure of a 2D material. In this talk, We will show several examples of 2D materials with pentagonal structure, which are predicted to possess a variety of attractive properties from being an antiferromagnetic and semiconducting to the rare coexistence of ferromagnetic ordering and piezoelectricity.

9:20 AM  
Computational Methodological Study of Mn(taa) Spin Crossover Compound: Eric Fonseca1; Daniel Rodriguez1; Samuel Trickey1; Richard Hennig1; 1University of Florida
    Single molecular magnets are being explored for their utility in computers which embrace quantum mechanical effects. These compounds exhibit magnetic bistability, as they can exist stably in two different spin states, making them candidates for magnetic memory. A study of MnIII(taa) was conducted in order to determine the influence of computational methods on spin-crossover energy and ligand field splitting. These computational methods were explored in order to identify the appropriate method for studying these compounds. Calculations were systematically conducted which utilized functional choice (PBE/SCAN) and tested results with the inclusion of Van Der Waals forces and DFT+U. By utilizing linear response theory, the onsite Hubbard U value of the MnIII ion was determined self-consistently for PBE+U and SCAN+U. Preliminary results indicate that the SCAN functional on its own incorrectly predicts features of this system. However, the results are improved for all calculation combinations when dispersion relations are included.

9:40 AM  
First-principles Investigation of Dopants, Defects, and Defect Complexes in 2D Transition Metal Dichalcogenides: Anne Marie Tan1; Christoph Freysoldt2; Richard Hennig1; 1University of Florida; 2Max-Planck-Institut für Eisenforschung GmbH
    Two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDCs) have attracted extensive research interests for potential applications in optoelectronics, spintronics, photovoltaics, and catalysis. Understanding the effect of impurities, defects, and dopants on their electronic properties is crucial for the selection of materials and choice of suitable synthesis and processing conditions. We perform density functional theory calculations to accurately compute formation energies and charge transition levels associated with dopants, defects, and defect complexes in technologically relevant TMDCs such as MoS2 and WSe2. We utilize a correction scheme developed by Freysoldt and Neugebauer to ensure the appropriate electrostatic boundary conditions for charged defects in 2D materials. We identify dopants which can form defect complexes with intrinsic defects such as vacancies, acting as compensating defects and modifying the electronic properties of the individual defects. By analyzing the defect electronic structures, we predict that phenomena such as Jahn-Teller distortions help stabilize defects in particular charge states.

10:00 AM  
Machine Learned Models for Transition Metal Dichalcogenide: Henry Chan1; Mathew Cherukara1; Badri Narayanan2; Subramanian Sankaranarayanan1; 1Argonne National Laboratory; 2University of Louisville
    Transition metal dichalcogenide (TMD) are novel nanomaterials that can behave like conductors, semiconductors, or insulators depending on the type of transition metal used. With a thickness as small as three atoms and size dependent properties, TMDs have a great potential in applications such as flexible and wearable electronics. Despite the early discovery of TMDs and their synthesis via vapor deposition, fundamental understanding on their growth mechanisms remains largely unknown, which hindered the preparation of these materials on a larger scale. Molecular simulations can be used to address this problem, but the lack of accurate interatomic potentials and the large effort required to develop them presents a major barrier. Here, we demonstrate the use of a machine learning based framework in the development of a reactive force field model for tungsten diselenide. Our data-driven procedure led to models that accurately captures various properties, including structures, dynamical stability, phonon, and various energetics.

10:20 AM Break

10:35 AM  Invited
Predicting Physical Properties of SiO2-based Glasses by Machine Learning: Yong-Jie Hu1; Ge Zhao2; Bo Liu1; Yang Chen1; Kerby Shedden1; Liang Qi1; 1University of Michigan; 2Pennsylvania State University
    SiO2-based glasses have diverse applications as both structural and functional materials. It is difficult to efficiently predict and optimize their physical properties according to the chemical composition due to their non-crystalline structures. We have developed machine learning (ML) techniques to predict their physical properties across a complex compositional space. For densities and elastic moduli of glasses, our approach relies on a training set generated by high-throughput molecular dynamic simulations and descriptors based on fundamental physics of interatomic bonding. For properties that are difficult to be calculated by atomistic simulations, such as liquidus temperatures and dielectric constants, the training sets are directly obtained from available glass property databases such as SciGlass and descriptors based on crystalline oxide properties from first-principles calculations. We also applied ML models to generate a compositional-property database that allows for a fruitful overview of the physical properties of the general multi-component glass systems.

11:05 AM  
Stable Structures of 2D Materials, Thin Films, and Surface Reconstructions on Substrates using an Evolutionary Algorithm Approach: Venkata Surya Kolluru1; Pushkar Ghanekar2; Jeffrey Greeley2; Richard Hennig1; 1University of Florida; 2Purdue University
     We modified the genetic algorithm for structure prediction (GASP) to find stable substrates for known 2D materials and determine the structural changes induced in 2D films due to the substrate. We implemented a lattice matching algorithm to match the 2D lattice to the selected substrate within a prescribed strain and maximum surface area. The structures are assigned values based on energy and a selected objective function based on adsorption energy per surface atoms or area. We control exploration and exploitation of the potential energy landscape and find low energy structures.We test our algorithm for the Si (100) surface and correctly predicted the most stable dimer configuration along with close competitors. Currently, we are testing the algorithm with multi-species systems. Stable structures are further investigated at different chemical potentials for analysis of structure change mechanisms. This software package would help accelerate the experimental synthesis of 2D materials and films.

11:25 AM  
Landscape Study of Deformation Effects (cleave/shear) and Vacancies on the Structural Electronic and Mechanical Properties of MAX Phase Alloys: Daniel Sauceda1; Prashant Singh1; Raymundo Arroyave1; 1Texas A&M University
    We present a systematic study of structural, electronic and mechanical properties of cleaved and sheared MAX phase (M 2 SiC: M= Sc, Ti, V, Cr, Mn, Fe, Co, Cu, Mo, Nb, Ni, Y, Zr, Ta, Hf, Ta) using first-principles density functional theory. Our focus remains on the cleave/shear (simple alias) deformation of M-A layer as the weaker metallic bonds between M-A make the deformation easier. Interestingly, we directly connect the mechanical behavior of cleaved/sheared M 2 SiC to changing interstitial electron-counts and atomic-size while moving across the periods and groups of the periodic table, respectively. The predicted elastic constants obey the well-known Born stability criteria and indicate the mechanical stability as a function of the cleave/shear distance (0-3 Å). The Pugh’s ratio (G/K) has been used to establish ductility or brittleness. We also discuss the effect of vacancies on the structural, electronic and mechanical properties of M2SiC MAX Phases.

11:45 AM  
Tuning Mechanical Behavior of Graphene: From Microscopic Defect Modeling to Macroscopic Property Prediction: Bowen Zheng1; Grace Gu1; 1University of California, Berkeley
    Understanding defect behavior in graphene enables on-demand manipulation of mechanical properties for various applications. In this study, microscopic defect behaviors and their influence on the macroscopic mechanical properties of graphene are investigated via molecular dynamics simulation. Fundamental insights on the mechanics of defects are provided by analyzing stress fields, which enables the identification and prediction of defect interaction and structural failure. Bridging detailed defect behaviors to macroscopic mechanical properties, the proposed multiscale paradigm is capable of investigating two novel attributes of defective graphene: tuning mechanical anisotropy and recovering from defect-induced mechanical degradation. Finally, a machine-learning approach is applied not only to create optimized defect patterns that lead to superior mechanical properties, but also to predict fracture behaviors such as initiation location and propagation pattern. This work may open up new possibilities to enhance the performance of graphene-based applications such as stretchable electronics and supercapacitor devices.