Semimetallic carbon allotrope with topological nodal line in mixed sp2sp^2-sp3sp^3 bonding networks

Graphene is known as a two-dimensional Dirac semimetal, in which electron states are described by the Dirac equation of relativistic quantum mechanics. Three-dimensional analogues of graphene are characterized by Dirac points or lines in momentum space, which are protected by symmetry. Here, we report a novel 3D carbon allotrope belonging to a class of topological nodal line semimetals, discovered by using an evolutionary structure search method. The new carbon phase in monoclinic $C$2$/m$ space group, termed $m$-$C_8$, consists of five-membered rings with $sp^3$ bonding interconnected by $sp^2$-bonded carbon networks. Enthalpy calculations reveal that $m$-$C_8$ is more favorable over recently reported topological semimetallic carbon allotropes, and the dynamical stability of $m$-$C_8$ is verified by phonon spectra and molecular dynamics simulations. Simulated x-ray diffraction spectra propose that $m$-$C_8$ would be one of the unidentified carbon phases observed in detonation shoot. The analysis of electronic properties indicates that $m$-$C_8$ exhibits the nodal line protected by both inversion and time-reversal symmetries in the absence of spin-orbit coupling and the surface band connecting the projected nodal points. Our results may help design new carbon allotropes with exotic electronic properties.Comment: 18 pages, 5 figure

Structure prediction of crystals, surfaces and nanoparticles

We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning. We illustrate the current status of the field with examples taken from metallic, inorganic and organic systems. This article is part of a discussion meeting issue 'Dynamic in situ microscopy relating structure and function'

Structure prediction of crystals, surfaces and nanoparticles

We review the current techniques used in the prediction of crystal structures and their surfaces and of the structures of nanoparticles. The main classes of search algorithm and energy function are summarized, and we discuss the growing role of methods based on machine learning. We illustrate the current status of the field with examples taken from metallic, inorganic and organic systems. This article is part of a discussion meeting issue ‘Dynamic in situ microscopy relating structure and function’

Structure Prediction Drives Materials Discovery

Progress in the discovery of new materials has recently been driven by the development of reliable quantum-mechanical approaches to crystal structure prediction. The properties of a material depend very sensitively on its structure, and therefore structure prediction is the key to computational materials discovery. Structure prediction was considered to be a formidable problem, but the development of new computational tools has allowed the structures of many new and increasingly complex materials to be anticipated. These widely applicable methods, based on global optimisation and relying on little or no empirical knowledge, have been used to study crystalline structures, point defects, surfaces and interfaces. In this review we present examples of computationally-driven discovery of new materials that will enable new technologies and lead to a better understanding of physical and chemical phenomena in materials

Computational Molecular Design of Polyhedral Oligomeric Silsesquioxane Based Organic-Inorganic Hybrid Semiconductors.

Cubic silsesquioxanes (T8 SQs), with the formula of [RSiO1.5]8, enable advanced materials design. In this thesis, a computational materials science framework, including ab initio density functional theory (DFT) calculations, molecular dynamics (MD), and Monte Carlo (MC) simulations, was developed to perform computational molecular design and crystal engineering of SQ based diacene-SQ and then octa(halogenphenyl)-SQ molecular systems. The goal of this project was to identify novel molecular architectures, a priori, that exhibit targeted self-assembly behaviors and result in materials with improved electronic properties. First, existing force fields, including our in house charge transfer reactive (CTR) force field, and COMPASS, were evaluated for simulating cubic SQ systems. All force fields reproduced the experimental structure of SQ-based crystals very well. However, only the FLX force field reproduced the experimentally observed vibrational properties and thermodynamic behavior. Next, targeting materials performance, such as high electronic mobility, a series of diacene-SQ molecules were designed and their crystal structures predicted by following the computational molecular design recipe that accounts for transport theory, symmetry relationships, polymorph prediction procedures, and solid state electronic property evaluation methods. Computationally derived diacene-SQ crystals are predicted to exhibit advanced electronic properties, such as very small band gaps and parallel packing of the acene groups in crystal structures, indicating excellent transport properties, as well as improved thermal and mechanical properties. Finally, a series of new small-band gap octa(halogenphenyl)-SQ molecular systems were identified by computationally exploring alternative architectures and functionalization of recently synthesized octa(halogenphenyl)-SQ crystals. These hybrid molecular crystals also feature other unique properties, such as solution processability, cubic molecular symmetry, and the three-dimensional conjugation. The computationally designed octa(2,5-diiodophenyl)-SQ (ODIPS) shows a calculated conduction band structure similar to that of 1,4-diiodobenzene (DIB), whose high hole mobility is known from experiment. Electronic band structure calculations indicate that the SQ cages, which are by themselves insulators, contribute to the electronic transport process in these hybrid molecules, and enhance the intrinsic electronic properties of the organic semiconductor functional groups.Ph.D.Materials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/86289/1/fqi_1.pd

Multiscale Modeling of Structure-Function Relationships of Organic Semiconductors

While optoelectronic devices built from organic semiconductors (OSC) continue to find their way into the commercial landscape, there remain numerous challenges to overcome to supplant traditional semiconductors in many technologies. Chief among these are low performance metrics relative to devices with conventional semiconductors and device stability. In order to overcome these challenges, a wide range of new OSC and processing technologies have been developed. However, there remains limited fundamental understanding of the relationship between molecular structure, packing in the solid state, and the resulting materials properties. Here, we make use of multiscale molecular models and utilize classical all-atom molecular dynamic (MD) simulations and quantum-chemical density functional theory (DFT) to explore the molecular structure-function relationships. Our results have provided distinctive insights into how the chemistries and processing of the OSC impact the material characteristics

Accuracy and Transferability in Machine Learned Potentials for Carbon

In this thesis, we discuss the approach taken to construct an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. We begin by discussing the process for constructing a potential for a single phase, graphene. We then extend this to produce a general-purpose potential, named GAP-20, which describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with a high degree of accuracy. We combine structural databases for amorphous carbon and graphene, which we extend substantially by adding suitable configurations, for example, for defects in graphene and other nanostructures. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional. Dispersion interactions, which are crucial to describe multilayer carbonaceous materials, are therefore implicitly included. We additionally account for long-range dispersion interactions using a semianalytical two-body term and show that an improved model can be obtained through an optimization of the manybody smooth overlap of atomic positions descriptor. We rigorously test the potential on lattice parameters, bond lengths, formation energies, and phonon dispersions of numerous carbon allotropes. We compare the formation energies of an extensive set of defect structures, surfaces, and surface reconstructions to DFT reference calculations. The present work demonstrates the ability to combine, in the same ML model, the previously attained flexibility required for amorphous carbon with the high accuracy necessary for crystalline graphene which we introduce in this thesis, thereby providing an interatomic potential that will be applicable to a wide range of applications concerning diverse forms of bulk and nanostructured carbon

Artificial Intelligence in Materials Science: Applications of Machine Learning to Extraction of Physically Meaningful Information from Atomic Resolution Microscopy Imaging

Materials science is the cornerstone for technological development of the modern world that has been largely shaped by the advances in fabrication of semiconductor materials and devices. However, the Moore’s Law is expected to stop by 2025 due to reaching the limits of traditional transistor scaling. However, the classical approach has shown to be unable to keep up with the needs of materials manufacturing, requiring more than 20 years to move a material from discovery to market. To adapt materials fabrication to the needs of the 21st century, it is necessary to develop methods for much faster processing of experimental data and connecting the results to theory, with feedback flow in both directions. However, state-of-the-art analysis remains selective and manual, prone to human error and unable to handle large quantities of data generated by modern equipment. Recent advances in scanning transmission electron and scanning tunneling microscopies have allowed imaging and manipulation of materials on the atomic level, and these capabilities require development of automated, robust, reproducible methods.Artificial intelligence and machine learning have dealt with similar issues in applications to image and speech recognition, autonomous vehicles, and other projects that are beginning to change the world around us. However, materials science faces significant challenges preventing direct application of the such models without taking physical constraints and domain expertise into account.Atomic resolution imaging can generate data that can lead to better understanding of materials and their properties through using artificial intelligence methods. Machine learning, in particular combinations of deep learning and probabilistic modeling, can learn to recognize physical features in imaging, making this process automated and speeding up characterization. By incorporating the knowledge from theory and simulations with such frameworks, it is possible to create the foundation for the automated atomic scale manufacturing

The 2019 materials by design roadmap

Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. The increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this Roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. The various perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this Roadmap will guide researchers and funding agencies in identifying new prospects for materials design