arXiv subjects

Harley T. Johnson

Publications and source records attributed to Harley T. Johnson.

9 recordsLinked to original sources

arXiv2025

Probing Topological Surface States and Conduction via Extended Defects in (Bi$_{1-x}$Sb$_x$)$_2$Te$_3$ Films

(Bi$_{1-x}$Sb$_x$)$_2$Te$_3$ alloys are non-degenerate topological insulators (TIs) whose Dirac point (DP) can be tuned within the bulk bandgap by varying the composition, effectively reducing bulk conduction while allowing surface carrier conduction. Magnetotransport measurements of a series of (Bi$_{1-x}$Sb$_x$)$_2$Te$_3$ thin films indicate electron-dominated conduction, with weak anti-localization attributed to topological surface states (TSSs). Due to the similarity of phase coherence lengths and twin boundary spacings ($\sim$100 nm), we consider the role of twin boundaries as additional conducting paths. Density functional theory calculations reveal an enhanced density of states near the Fermi level at $60^\circ$ twin boundaries, with 2D carrier concentration in excess of $3 \times 10^{13}$ cm$^{-2}$. Furthermore, an analysis of the longitudinal magnetoconductivity yields an upper bound of $7.3 \times 10^{-4}$ S for twin boundary conductivity, resulting in a carrier mobility as high as $142$ cm$^2$/(V$\cdot$s). We discuss the role of twin boundaries in facilitating a transition from a massive Dirac cone dispersion to gapless, topologically protected surface states. Understanding the role of twin boundaries on carrier conduction in non-degenerate TIs is critical for the development of novel TI-based electronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Interatomic potential development for topological insulator Bi1-xSbx and its dislocation by force-following active learning

We introduce a force following active learning algorithm that integrates density functional theory DFT with the Gaussian Approximation Potential GAP framework to develop a robust interatomic potential IP for a dislocation in a topological insulator Bi1xSbx. Starting from an initial potential IP0 trained on unit cell data from strained Bi Sb binaries our active learning approach iteratively refines the IP during a structural relaxation. In each cycle if the force error uncertainty of any atom near the dislocation core exceeds a threshold value the IPi is efficiently retrained IPi to IPi1 by incorporating DFT computed forces and energies of atoms near the high uncertainty atom. This strategy ensures that the relaxation process maintains a low force error until full convergence is achieved. Consequently the final IP here IP5 has two capabilities 1 it reproduces the relaxation pathway observed during the active learning process unlike the initial IP0 which lacks prior dislocation core knowledge and 2 it captures the lattice and elastic properties of Bi Sb binaries across a range of Sb concentrations. We also evaluate dislocation properties Peierls stresses and dislocation generation by compression to assess the performance of the trained potential IP5.

cond-mat.mtrl-sci↗

arXiv2025

Graph Neural Network for Unified Electronic and Interatomic Potentials: Strain-tunable Electronic Structures in 2D Materials

We introduce UEIPNet, an equivariant graph neural network designed to predict both interatomic potentials and tight-binding (TB) Hamiltonians for an atomic structure. The UEIPNet is trained using density functional theory calculations followed by Wannier projection to predict energies and forces as node-level targets and Wannier-projected TB matrices as edge-level targets. This enables physically consistent modeling of coupled mechanical electronic responses with near-DFT accuracy. Trained on bilayer graphene and monolayer MoS2 DFT data, UEIPNet captures key deformation-electronic effects: in twisted bilayer graphene, it reveals how interlayer spacing, in-plane strain, and out-of-plane corrugation drive isolated flat-band formation, and further shows that modulating substrate interaction strength can generate flat bands even away from the magic angle. For monolayer MoS2, the UEIPNet accurately reproduces phonon dispersions, strain-dependent band-gap evolution, and local density of states modulations under non-uniform strain. The UEIPNet offers a generalized, efficient, and scalable framework for studying deformation-electronic coupling in large-scale atomistic systems, bridging classical atomistic simulations and electronic-structure calculations.

cond-mat.mtrl-sci↗

arXiv2025

Graphene-hBN interlayer interactions from quantum Monte Carlo

The interaction between graphene and hexagonal boron nitride (hBN) plays a pivotal role in determining the electronic and structural properties of graphene-based devices. In this work, we employ quantum Monte Carlo (QMC) to study the interlayer interactions and stacking-fault energy (SFE) between graphene and hBN. We generated QMC energies for several rigid bilayer stacking configurations and fitted these data to the Kolmogorov-Crespi type interlayer potential (ILP) model. Our QMC-derived potential offers a more reliable alternative to conventional density functional theory methods, which are prone to errors in predicting properties in van der Waals materials. This study enables highly accurate predictions of structural and electronic properties in graphene hBN heterostructures. The resulting ILP-QMC potential is made available for further use in simulating complex systems, such as twisted bilayer graphene (TBG) on hBN.

cond-mat.mtrl-sci↗

arXiv2024

Fundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends

Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs) provide an avenue to predictive materials discovery. We propose that such correlations can be efficiently discovered through simulations utilizing approximate interatomic potentials (IPs), which serve as an ensemble of "synthetic materials." As a proof of principle we build a regression model relating canonical properties to the symmetric tilt grain boundary (GB) energy curves in face-centered cubic crystals, characterized by the scaling factor in the universal lattice matching model of Runnels et al. (2016), which we take to be our QoI. Our analysis recovers known correlations of GB energy to other properties and discovers new ones. We also demonstrate, using available density functional theory (DFT) GB energy data, that the regression model constructed from IP data is consistent with DFT results, confirming the assumption that the IPs and DFT belong to same statistical pool and thereby validating the approach. Regression models constructed in this fashion can be used to predict large-scale QoIs based on first-principles data and provide a general method for training IPs for QoIs beyond the scope of first-principles calculations.

cond-mat.mtrl-sci↗

arXiv2023

Registry-dependent potential energy and lattice corrugation of twisted bilayer graphene from quantum Monte Carlo

An uncertainty in studying twisted bilayer graphene (TBG) is the minimum energy geometry, which strongly affects the electronic structure. The minimum energy geometry is determined by the potential energy surface, which is dominated by van der Waals (vdW) interactions. In this work, large-scale diffusion quantum Monte Carlo (QMC) simulations are performed to evaluate the energy of bilayer graphene at various interlayer distances for four stacking registries. An accurate registry-dependent potential is fit to the QMC data and is used to describe interlayer interactions in the geometry of near-magic-angle TBG. The band structure for the optimized geometry is evaluated using the accurate local-environment tight-binding model. We find that compared to QMC, DFT-based vdW interactions can result in errors in the corrugation magnitude by a factor of 2 or more near the magic angle. The error in corrugation then propagates to the flat bands in twisted bilayer graphene, where the error in corrugation can affect the bandwidth by about 30\% and can change the nature and degeneracy of the flat bands.

cond-mat.mes-hall↗

arXiv2021

An accurate tight binding model for twisted bilayer graphene describes topological flat bands without geometric relaxation

A major hurdle in understanding the phase diagram of twisted bilayer graphene (TBLG) are the roles of lattice relaxation and electronic structure on isolated band flattening near magic twist angles. In this work, the authors develop an accurate local environment tight binding model (LETB) fit to tight binding parameters computed from $ab\ initio$ density functional theory (DFT) calculations across many atomic configurations. With the accurate parameterization, it is found that the magic angle shifts to slightly lower angles than often quoted, from around 1.05$^\circ$ to around 0.99$^\circ$, and that isolated flat bands appear for rigidly rotated graphene layers, with enhancement of the flat bands when the layers are allowed to distort. Study of the orbital localization supports the emergence of fragile topology in the isolated flat bands without the need for lattice relaxation.

cond-mat.mes-hall↗

arXiv2020

Selection rules of twistronic angles in 2D material flakes via dislocation theory

Interlayer rotation angle couples strongly to the electronic states of twisted van der Waals layers. However, not every angle is energetically favorable. Recent experiments on rotation-tunable electronics reveal the existence of a discrete set of angles at which the rotation-tunable electronics assume the most stable configurations. Nevertheless, a quantitative map for locating these intrinsically preferred twist angles in twisted bilayer system has not been available, posing challenges for the on-demand design of twisted electronics that are intrinsically stable at desired twist angles. Here we reveal a simple mapping between intrinsically preferred twist angles and geometry of the twisted bilayer system, in the form of geometric scaling laws for a wide range of intrinsically preferred twist angles as a function of only geometric parameters of the rotating flake on a supporting layer. We reveal these scaling laws for triangular and hexagonal flakes since they frequently appear in chemical vapor deposition growth. We also present a general method for handling arbitrary flake geometry. Such dimensionless scaling laws possess universality for all kinds of two-dimensional material bilayer systems, providing abundant opportunities for the on-demand design of intrinsic "twistronics". For example, the set of increasing magic-sizes that intrinsically prefers zero-approaching sequence of multiple magic-angles in bilayer graphene system can be revealed.

cond-mat.mes-hall↗

arXiv2018

Strain Modulation of Graphene by Nanoscale Substrate Curvatures: A Molecular View

Spatially nonuniform strain is important for engineering the pseudomagnetic field and band structure of graphene. Despite the wide interest in strain engineering, there is still a lack of control on device-compatible strain patterns due to the limited understanding of the structure-strain relationship. Here, we study the effect of substrate corrugation and curvature on the strain profiles of graphene via combined experimental and theoretical studies of a model system: graphene on closely packed SiO2 nanospheres with different diameters (20-200 nm). Experimentally, via quantitative Raman analysis, we observe partial adhesion and wrinkle features and find that smaller nanospheres induce larger tensile strain in graphene, theoretically, molecular dynamics simulations confirm the same microscopic structure and size dependence of strain and reveal that a larger strain is caused by a stronger, inhomogeneous interaction force between smaller nanospheres and graphene. This molecular-level understanding of the strain mechanism is important for strain engineering of graphene and other two-dimensional materials.

cond-mat.mes-hall↗