Computational Materials Discovery and Optimization – From Bulk to Materials Interfaces and 2D Materials: Electronic, Magnetic, and Optical Properties
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS: Computational Materials Science and Engineering Committee
Program Organizers: Richard Hennig, University of Florida; Arunima Singh, Lawrence Berkeley National Laboratory; Dallas Trinkle, University of Illinois, Urbana-Champaign; Eric Homer, Brigham Young University
Tuesday 2:00 PM
February 28, 2017
Location: San Diego Convention Ctr
2:00 PM Invited
Bridging Semi-classical and Ab Initio Descriptions of Electronic Transport in Semiconductors: Alireza Faghaninia1; Michael Sullivan1; Derreko Becker-Ricketts1; Cynthia Lo1; 1Washington University
Accurate models for electron and hole transport are necessary for the calculation of microscopic properties that dictate macroscopic behavior in degenerate semiconductors. We propose to bridge the quantum-classical divide by 1: Reformulating the model parameters in the semi-classical elastic scattering rate expressions to use band structure and density of states information calculated ab initio, without reliance on experimental data for model fitting, and 2. Explicitly considering the inelastic electron-phonon interactions. First, we demonstrate the validity of our model, AMSET (ab initio model for calculation of Mobility and Seebeck coefficient using Boltzmann Transport equation), on a variety of compound n-type and p-type semiconductors. Next, we show how AMSET enables us to identify the most promising transparent conducting oxides, based on high-throughput screening. Finally, we explore the limitations of the semi-classical Boltzmann transport equation in the case of barium stannate, which can be prepared with extremely high charge carrier densities.
2:30 PM Invited
Using First Principle Approaches to Optimize Materials for Next Generation Non-volatile Memory: Derek Stewart1; 1Western Digital
Non-volatile oxide-based resistive RAM devices offer one promising route to continue Moore’s law and circumvent problems at small scales that have plagued traditional memory devices (DRAM, SRAM, and Flash). The RRAM ON and OFF resistance states are defined by the formation and destruction of a conductive vacancy filament in the oxide under applied bias. Given the complexity of this memory switching phenomena, first principle calculations can provide critical insight into the local electronic structure and defect formation and diffusion. I will discuss our recent work using different material combinations and dopants to optimize vacancy formation and diffusion in RRAM oxides (HfO2, Ta2O5). We examine a broad range of dopants from the periodic table and identify clear trends due to dopant valence charge and orbital character. I will also discuss how atomic disorder, electrode material and interface layers can affect vacancy dynamics, electronic transport, and device performance.
Neural Networks Assisted Tomographic Reconstruction of Electrostatic Potential: KC Prabhat1; Marc De Graef1; 1Carnegie Mellon University
A proper tomographic reconstruction becomes increasingly challenging when the number of projections is limited. This is also the case when projections are acquired using Lorentz Transmission Electron Microscopy (LTEM) for the reconstruction of the electromagnetic potentials of magnetic nanoparticles. Even the most advanced TEM holder, built specifically for the purpose of tomography, does not allow performing a full 180° tilt series. This leads to a poor reconstruction with sample edge artifacts. Hence, we propose to use neural networks to overcome the issue of limited projections. In this method, we will, first, generate simulated projections using the Aharonov-Bohm magnetic phase shift expression. Next, we will apply an array of filters ranging from low-pass to high-pass cutoff frequency to generate a training set. Subsequently, neural networks will be used to non–linearly weigh each of the filters such that the final scaling suppresses the reconstruction edge artifacts.
First-Principles Computation Design of CoPt and FePt Nanoparticles with Desired Magnetic Properties through Tailoring Surface Segregation: Guofeng Wang1; Zhenyu Liu1; 1University of Pittsburgh
Surface segregation leads to chemical disordering in magnetic alloy nanostructures and thus has profound impact upon the magnetic properties of these nanostructures. In this study, we used the first-principles density functional theory (DFT) calculation method to investigate how surface segregation would affect the magnetic properties of L10 ordered CoPt and FePt nanoparticles. Comparing the magnetic properties of bulk-terminated and surface-segregated nanoparticles, we predicted that the surface segregation in the small CoPt and FePt nanoparticles could cause a decrease in their total magnetic moments, a change in their (easy and/or hard) magnetization axes, and a reduction in their magnetic anisotropy. To design CoPt and FePt nanoparticles with desired magnetic properties, we further performed DFT calculations to study how the surface segregation and thus magnetic properties of CoPt and FePt nanoparticles can be tailored by doping of Cu and varying particle shapes.
3:40 PM Break
3:55 PM Invited
Magnetic-Field Tunability of Thermal Conduction in Non-Magnetic Materials: Wolfgang Windl1; Nikolas Antolin1; Oscar Restrepo1; Roberto Myers1; Joseph Heremans1; 1Ohio State Univ.
One of the grand challenges in the field of computational materials is the prediction of properties from first principles as basis for property-driven materials design. While prediction of “bare” transport properties are already very challenging, this becomes especially difficult for field-tunable property changes. Here, we describe a computational approach to describe from first principles how magnetic fields can change thermal conduction in non-magnetic semiconductors. For the case of InSb, a change of more than 10% was observed in experiments. Thermal conductivity is limited by phonon-phonon scattering due to phonon anharmonicities. Using ab-initio calculations, we find that the observed additional anharmonic interaction from the magnetic field is consistent with a local phonon-induced diamagnetic susceptibility. The theoretical predictions are in excellent quantitative agreement with experiment. This work was funded by the Center for Emergent Materials, an NSF MRSEC at OSU (Grant DMR- 1420451).
4:25 PM Cancelled
Data-driven Magnetic Materials Selection, Design, and Optimization: Shruthi Badam1; Tanjore Jayaraman1; 1University of Michigan
Magnetic materials are ubiquitous and, are used in countless applications including, but not limited to computers, clean and green energy technologies, and defense systems. Over the years a variety of magnetic materials have been developed adopting several of the processing techniques, in various forms, to meet the imminent technological demands. It is imperative to integrate materials-informatics to enable efficient and high-throughput screening and designing of novel magnetic materials. In this work a combination of—various multiple attribute decision making (MADM) methods, cluster expansion, principal component analysis (PCA), and partitioning were adopted for magnetic materials selection, design, and properties optimization. The magnetic materials—hard and soft—were evaluated using various MADMs. Suitable objective methods were adopted to evaluate the relative weights of various properties (attributes). Correlation among the properties were calculated and hierarchical clustering was adopted for classification. Finally, a decision-tree based model was developed using PCA and partitioning.
Optimization of Buffer Layer Alloy Materials for CIGS Thin-Film Solar Cells: Vincenzo Lordi1; Joel Varley1; Xiaoqing He2; Angus Rockett2; Jeff Bailey3; Geordie Zapalac3; Dmitry Poplavskyy3; Neil Mackie3; Atiye Bayman3; 1Lawrence Livermore National Lab; 2University of Illinois at Urbana-Champaign; 3MiaSole Hi-Tech Corp.
Achieving maximum efficiency CuInGaSe2 solar cells requires optimization of the buffer layer deposited between absorber and transparent contact. Careful tradeoffs of band gap, conduction band offset, dopability, interface quality, and film crystallinity are required to simultaneously maximize carrier collection and transparency. We explore the (Cd,Zn)(O,S) alloy system using a combination of theory, synthesis, and characterization. Calculations were performed using hybrid density functional theory across the alloy composition range to search for optimal compositions based on a suite of computed properties. Critical aspects related to elemental intermixing across the absorber/buffer interface and secondary phase formation are also predicted. Devices were fabricated in a commercial production line using a continuous sputter process, with variations to produce films with different composition profiles and crystallinity, and analyzed with cross-section transmission electron microscopy. Correlation of predicted and observed properties allows rational optimization of material and devices. Prepared by LLNL under Contract DE-AC52-07NA27344.
Restraining Electron-hole Recombination in W-N Codoped Titania: First-principles Study: Heechae Choi1; 1Virtual Lab Inc.
Tungsten-nitrogen (W-N) codoping has been known to enhance the photocatalytic activity of anatase TiO2 nanoparticles by utilizing visible light. The doping effects are, however, largely dependent on calcination or annealing conditions, and thus, the massive production of quality-controlled photocatalysts still remains a challenge. Using density functional theory (DFT) thermodynamics and time-dependent DFT (TDDFT) computations, we investigate the atomic structures of N doping and W-N co-doping in anatase TiO2 , as well as the effect of the thermal processing conditions on the photocatalytic activities. Our finding from DFT and TDDFT calculations successfully unveiled the unexplained variations of optical properties and photocatalytic activities of W-N codoped anatase TiO2.