A library of ab initio Raman spectra for automated identification of 2D materials

Raman spectroscopy is frequently used to identify composition, structure and layer thickness of 2D materials. Here, we describe an efficient first-principles workflow for calculating resonant first-order Raman spectra of solids within third-order perturbation theory employing a localized atomic orbital basis set. The method is used to obtain the Raman spectra of 733 different monolayers selected from the computational 2D materials database (C2DB). We benchmark the computational scheme against available experimental data for 15 known monolayers. Furthermore, we propose an automatic procedure for identifying a material based on an input experimental Raman spectrum and illustrate it for the cases of MoS$_2$ (H-phase) and WTe$_2$ (T$^\prime$-phase). The Raman spectra of all materials at different excitation frequencies and polarization configurations are freely available from the C2DB. Our comprehensive and easily accessible library of \textit{ab initio} Raman spectra should be valuable for both theoreticians and experimentalists in the field of 2D materialsComment: 17 pages, 7 figure

Thermal Conductivity of Complex Crystals, High Temperature Materials and Two Dimensional Layered Materials

Thermal conductivity is a critical property for designing novel functional materials for engineering applications. For applications demanding efficient thermal management like power electronics and batteries, thermal conductivity is a key parameter affecting thermal designs, stability and performances of the devices. Thermal conductivity is also the critical material metrics for applications like thermal barrier coatings (TBCs) in gas turbines and thermoelectrics (TE). Therefore, thermal conductivities of various functional materials have been investigated in the past decade, but most of the materials are simple and isotropic crystals at low temperature. This is because the first-principles calculation is limited to simple crystals at ground state and few experimental methods are only capable of measuring thermal conductivity along a single direction. The objective of this thesis is to develop first-principles based atomistic modeling tools to study thermal conductivity and phonon properties of complex crystals, high temperature materials, as well as and ultrafast laser based pump-probe techniques to characterize anisotropic thermal conductivity of layered two-dimensional materials. In the first part of this thesis, an integrated density functional theory and molecular dynamics (DFT-MD) method is developed to model the thermal conductivity and phonon properties of hybrid organic-inorganic crystals, a special kind of complex crystals integrating both organic molecules and inorganic frameworks. This DFT-MD method first develops an empirical potential field from first-principles DFT calculations, then predicts thermal conductivity using MD simulation. We applied this method to predict thermal conductivities of II-VI based hybrid crystals and organometal halide perovskites. An ultralow thermal conductivity (0.6 W/mK) is predicted in the perovskite CH3NH3PbI3, agreeing well with experimental measurements. In the second part, instead of using empirical functional forms, a data driven machine learning algorithm is used to develop high-fidelity potential field for phonon modeling. We demonstrated that the machine learning based potential is a powerful tool for modeling phonons at high temperature, even for dynamically unstable high-temperature phases, which is a challenging problem for both empirical potential based MD and static first-principles calculations. Using a simple machine learning algorithm called Gaussian process regression, we developed potential field that can effectively capture the stabilization of BCC phase of Zirconium at 1188 K, which is predicted to be unstable using static first-principles calculations. In the third part, a varied laser spot size technique based on time-domain thermoreflectance (TDTR) is developed to characterize anisotropic thermal conductivity. This method is applied to measure both the thermal conductivity parallel to the basal planes as well as the through-plane thermal conductivity of transition metal dichalcogenides, a group of layered two dimensional materials. Interestingly, the through-plane thermal conductivity is observed to decrease with the increasing heating frequency (modulation frequency of the pump laser) from 0.6 to 10 MHz, due to the non-equilibrium transport between different phonon modes. A two channel thermal model is developed to capture the non-equilibrium transport and to derive the thermal conductivity at local equilibrium. This finding suggest that in electronic devices working at a few GHz, the material could tend to become much more thermally insulating than steady state, raising great challenges for near junction thermal management.</p

The Computational 2D Materials Database: High-Throughput Modeling and Discovery of Atomically Thin Crystals

We introduce the Computational 2D Materials Database (C2DB), which organises a variety of structural, thermodynamic, elastic, electronic, magnetic, and optical properties of around 1500 two-dimensional materials distributed over more than 30 different crystal structures. Material properties are systematically calculated by state-of-the art density functional theory and many-body perturbation theory (G$_0\!$W\!_0 and the Bethe-Salpeter Equation for $\sim$200 materials) following a semi-automated workflow for maximal consistency and transparency. The C2DB is fully open and can be browsed online or downloaded in its entirety. In this paper, we describe the workflow behind the database, present an overview of the properties and materials currently available, and explore trends and correlations in the data. Moreover, we identify a large number of new potentially synthesisable 2D materials with interesting properties targeting applications within spintronics, (opto-)electronics, and plasmonics. The C2DB offers a comprehensive and easily accessible overview of the rapidly expanding family of 2D materials and forms an ideal platform for computational modeling and design of new 2D materials and van der Waals heterostructures.Comment: Add journal reference and DOI; Minor updates to figures and wordin

When Machine Learning Meets 2D Materials:A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.</p

2DMatPedia: An open computational database of two-dimensional materials from top-down and bottom-up approaches

Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of many studies. Here, we present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered structures by a topology-based algorithm, and theoretically exfoliate them into monolayers. Then, we generated new 2D materials by chemical substitution of elements in known 2D materials by others from the same group in the periodic table. The structural, electronic and energetic properties of these 2D materials are consistently calculated, to provide a starting point for further material screening, data mining, data analysis and artificial intelligence applications. We present the details of computational methodology, data record and technical validation of our publicly available data (http://www.2dmatpedia.org/)

Thermal Transport Across 2D/3D Van Der Waals Interfaces

Designing improved field-effect-transistors (FETs) that are mass-producible and meet the fabrication standards set by legacy silicon CMOS manufacturing is required for pushing the microelectronics industry into further enhanced technological generations. Historically, the downscaling of feature sizes in FETs has enabled improved performance, reduced power consumption, and increased packing density in microelectronics for several decades. However, many are claiming Moore\u27s law no longer applies as the era of silicon CMOS scaling potentially nears its end with designs approaching fundamental atomic-scale limits -- that is, the few- to sub-nanometer range. Ultrathin two-dimensional (2D) materials present a new paradigm of materials science and may pave the way for beyond-silicon CMOS technologies. Since the exfoliation of semi-metallic graphene in 2004, there have been discoveries of new families of semiconducting and insulating 2D materials that help realize fully-2D-based platforms, the study of novel quantum device physics, and provide new avenues in sensing and optical applications. However, selecting a new semiconducting channel material to design around is a highly non-trivial problem which requires finding a superlative candidate and then surrounding it with appropriate contacts (e.g., substrate) to ensure optimal performance. In modern microelectronics, a key feature for reliable performance is high interface thermal conductances so waste heat generated in device hot spots has a low-resistance pathway to thermal management hardware. Despite that importance, the study of interface thermal conductance between prospect 2D materials and their surrounding 3D contacts remains far behind the vast amount of literature covering their electrical and optical properties. This dissertation investigates interface thermal transport across mixed-dimensional 2D/3D van der Waals interfaces using a phonon Boltzmann transport model

Low-Dimensional Materials at the Nanoscale: Transition Metal Chalcogenides, Carbon Nanomaterials and Organic Semiconductors

The overall theme of this dissertation is the electronic transport and electromechanical study of low dimensional materials at the nanoscale. The dissertation is divided into three parts based on the class of materials: I. collective ground states in ultrathin materials, II. carbon nanomaterials based nanomechanical resonators and III. organic semiconductors. In part I, the superconductivity and charge density waves in transition metal chalcogenides are introduced. Crystal synthesis of transition metal chalcogenides by chemical vapor transport is presented. The materials have quasi-low dimensional crystal structure: either quasi-two dimensional (e.g. NbSe2, TaS2, WTe2, FeSe) or quasi-one dimensional (e.g. NbSe3, TaS3, (NbSe4)3I). Monolayer NbSe2, grown by molecular beam epitaxy, shows a superconducting transition at Tc=2K and is studied down to 50mK with magnetic fields. The sliding charge density waves in NbSe3 nanoribbons are studied with narrowband noise, which directly probes the order parameter. A proposal to scale down the contactless conductivity measurement technique for nanoscale samples with lithographically fabricated planar coils is presented.In part II, microstructures of suspended carbon nanotubes and graphene are studied as nanomechanical resonators. Carbon nanotubes are clamped on one end and the other end is free to enable field emission. The field emission provides a means of electrical readout. Fabrication of carbon nanotube field emitting mechanical resonators on an integrated platform are explored. The platform is designed to allow the study of the nanomechanical motion across multiple characterization techniques. Graphene nanomechanical resonators are studied as a first step in the development of a microactuator-based platform to control strain fields in graphene. In particular, non-uniaxial strains for large pseudo-magnetic field effects are intended.In part III, organic nanowire formation with DPP-TPA molecules for use in photovoltaics is explored. The nanowire’s charge carrier mobility is characterized in a field effect transistor. In addition, the use of rubrene single crystals for the study of photophysics at the interface with novel acceptor molecules is explored