Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning

We develop a computational database, web-apps and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D)-heterostructures. Using density functional theory (DFT) based lattice-parameters and electronic band-energies for 674 non-metallic exfoliable 2D-materials, we generate 226779 possible heterostructures. We classify these heterostructures into type-I, II and III systems according to Anderson rule, which is based on the band-alignment with respect to the vacuum potential of non-interacting monolayers.We find that type-II is the most common and the type-III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the periodic table of constituent elements. The band alignment data can be also used for identifying photocatalysts and high-work function 2D-metals for contacts.We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS2/WSe2, MoS2/h-BN, MoSe2/CrI3) to compare the band-alignment description with the predictions from Anderson rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we develop ML tools to predict band-alignment information for 2D materials. The web-apps, tools and associated data will be distributed through JARVIS-Heterostructure website (https://www.ctcms.nist.gov/jarvish).Our analysis, results and the developed web-apps can be applied to the screening and design applications, such as finding novel photocatalysts, photodetectors, and high-work function 2D-metal contacts

Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks

Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely Crystal Graph Convolutional Neural Network (CGCNN), Materials Graph Network (MEGNET), and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98 % given the range of values within the dataset, improving significantly on the state-of-the-art. Models are tested for different defect types as well as for defect charge transition levels. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially-optimized defective structures, helping identify electronically active defects in technologically-important semiconductors

Needs, trends, and advances in scintillators for radiographic imaging and tomography

Scintillators are important materials for radiographic imaging and tomography (RadIT), when ionizing radiations are used to reveal internal structures of materials. Since its invention by R\"ontgen, RadIT now come in many modalities such as absorption-based X-ray radiography, phase contrast X-ray imaging, coherent X-ray diffractive imaging, high-energy X- and $\gamma-$ray radiography at above 1 MeV, X-ray computed tomography (CT), proton imaging and tomography (IT), neutron IT, positron emission tomography (PET), high-energy electron radiography, muon tomography, etc. Spatial, temporal resolution, sensitivity, and radiation hardness, among others, are common metrics for RadIT performance, which are enabled by, in addition to scintillators, advances in high-luminosity accelerators and high-power lasers, photodetectors especially CMOS pixelated sensor arrays, and lately data science. Medical imaging, nondestructive testing, nuclear safety and safeguards are traditional RadIT applications. Examples of growing or emerging applications include space, additive manufacturing, machine vision, and virtual reality or `metaverse'. Scintillator metrics such as light yield and decay time are correlated to RadIT metrics. More than 160 kinds of scintillators and applications are presented during the SCINT22 conference. New trends include inorganic and organic scintillator heterostructures, liquid phase synthesis of perovskites and $\mu$m-thick films, use of multiphysics models and data science to guide scintillator development, structural innovations such as photonic crystals, nanoscintillators enhanced by the Purcell effect, novel scintillator fibers, and multilayer configurations. Opportunities exist through optimization of RadIT with reduced radiation dose, data-driven measurements, photon/particle counting and tracking methods supplementing time-integrated measurements, and multimodal RadIT.Comment: 45 pages, 43 Figures, SCINT22 conference overvie

First Principles Studies of ABO3 Perovskite Surfaces and Nanostructures

Perovskite-type complex oxides, with general formula ABO 3, constitute one of the most prominent classes of metal oxides which finds key applications in diverse technological fields. In recent years, properties of perovskites at reduced dimensions have aroused considerable interest. However, a complete atomic-level understanding of various phenomena is yet to emerge. To fully exploit the materials opportunities provided by nano-structured perovskites, it is important to characterize and understand their bulk and near-surface electronic structure along with the electric, magnetic, elastic and chemical properties of these materials in the nano-regime, where surface and interface effects naturally play a dominant role. In this thesis, state-of-the-art first principles computations are employed to systematically study properties of one- and two-dimensional perovskite systems which are of direct technological significance. Specifically, our bifocal study targets (1) polarization behavior and dielectric response of ABO3 ferroelectric nanowires, and (2) oxygen chemistry relevant for catalytic properties of ABO3 surfaces. In the first strand, we identify presence of novel closure or vortex-like polarization domains in PbTIO3 and BaTiO3 ferroelectric nanowires and explore ways to control the polarization configurations by means of strain and surface chemistry in these prototypical model systems. The intrinsic tendency towards vortex polarization at reduced dimensions and the underlying driving forces are discussed and previously unknown strain induced phase transitions are identified. Furthermore, to compute the dielectric permittivity of nanostructures, a new multiscale model is developed and applied to the PbTiO3 nanowires with conventional and vortex-like polarization configurations. The second part of the work undertaken in this thesis is comprised of a number of ab initio surface studies, targeted to investigate the effects of surface terminations, prevailing chemical environment and processing conditions on the surface relaxations, local electronic structure and chemical reactivity. By combining our first principles computations with an in-house developed kMC simulation approach, we describe the thermodynamics, steady-state kinetics and the long-time and large-length scale behavior of the catalytically active (001) MnO2-terminated LaMnO3 surface in contact with an oxygen reservoir, as a function of temperature and partial pressure of oxygen. The results obtained are in excellent agreement with available experimental data in the literature.

Ferromagnetism in IV main group element (C) and transition metal (Mn) doped MgO: A density functional perspective

The formation of magnetic moment due to the dopants with p-orbital (d-orbital) is named d0 (d −) magnetism, where the ion without (with) partially filled d states is found to be responsible for the observed magnetic properties. To study the origin of magnetism at a fundamental electronic level in such materials, as a representative case, we theoretically investigate ferromagnetism in MgO doped with transition metal (Mn) and non-metal (C). The generalized gradient approximation based first-principles calculations are used to investigate substitutional doping of metal (Mn) and non-metal (C), both with and without the presence of neighboring oxygen vacancy sites. Furthermore, the case of co-doping of (Mn, C) in MgO system is also investigated. It is observed that the oxygen vacancies do not play a role in tuning the ferromagnetism in presence of Mn dopants, but have a significant influence on total magnetism of the C doped system. In fact, we find that in MgO the d0 magnetism through C doping is curtailed by pairing of the substitutional dopant with naturally occurring O vacancies. On the other hand, in case of (Mn, C) co-doped MgO the strong hybridization between the C (2p) and the Mn(3d) states suggests that co-doping is a promising approach to enhance the ferromagnetic coupling between the nearest-neighboring dopant and host atoms. Therefore, (Mn,C) co-doped MgO is expected to be a ferromagnetic semiconductor with long ranged ferromagnetism and high Curie temperature