arXiv subjects

Richard G. Hennig

Publications and source records attributed to Richard G. Hennig.

At least 37 records · Page 2Linked to original sources

arXiv2024

Structural transitions, octahedral rotations, and electronic properties of $A_3$Ni$_2$O$_7$ rare-earth nickelates under high pressure

Motivated by the recent observation of superconductivity with $T_c \sim 80$ K in pressurized La3Ni2O7 [Nature 621, 493 (2023)], we explore the structural and electronic properties in A3Ni2O7 bilayer nickelates (A=La-Lu, Y, Sc) as a function of hydrostatic pressure (0-150 GPa) from first principles including a Coulomb repulsion term. At $\sim 20$ GPa, we observe an orthorhombic-to-tetragonal transition in La$_3$Ni$_2$O$_7$ at variance with recent x-ray diffraction data, which points to so-far unresolved complexities at the onset of superconductivity, e.g., charge doping by variations in the oxygen stoichiometry. We compile a structural phase diagram with particular emphasis on the $b/a$ ratio, octahedral anisotropy, and octahedral rotations. Intriguingly, chemical and external pressure emerge as two distinct and counteracting control parameters. We find unexpected correlations between $T_c$ and the in-plane Ni-O-Ni bond angles for La$_3$Ni$_2$O$_7$. Moreover, two novel structural phases with significant $c^+$ octahedral rotations and in-plane bond disproportionations are uncovered for A=Nd-Lu, Y, Sc that exhibit a surprising pressure-driven electronic reconstruction in the Ni $e_g$ manifold. By disentangling the involvement of basal versus apical oxygen states at the Fermi surface, we identify Tb$_3$Ni$_2$O$_7$ as an interesting candidate for superconductivity at ambient pressure. These results suggest a profound tunability of the structural and electronic phases in this novel materials class and are key for a fundamental understanding of the superconductivity mechanism.

cond-mat.supr-con↗

arXiv2024

Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function

Integrating deep learning with the search for new electron-phonon superconductors represents a burgeoning field of research, where the primary challenge lies in the computational intensity of calculating the electron-phonon spectral function, $α^2F(ω)$, the essential ingredient of Midgal-Eliashberg theory of superconductivity. To overcome this challenge, we adopt a two-step approach. First, we compute $α^2F(ω)$ for 818 dynamically stable materials. We then train a deep-learning model to predict $α^2F(ω)$, using an unconventional training strategy to temper the model's overfitting, enhancing predictions. Specifically, we train a Bootstrapped Ensemble of Tempered Equivariant graph neural NETworks (BETE-NET), obtaining an MAE of 0.21, 45 K, and 43 K for the Eliashberg moments derived from $α^2F(ω)$: $λ$, $ω_{\log}$, and $ω_{2}$, respectively, yielding an MAE of 2.5 K for the critical temperature, $T_c$. Further, we incorporate domain knowledge of the site-projected phonon density of states to impose inductive bias into the model's node attributes and enhance predictions. This methodological innovation decreases the MAE to 0.18, 29 K, and 28 K, respectively, yielding an MAE of 2.1 K for $T_c$. We illustrate the practical application of our model in high-throughput screening for high-$T_c$ materials. The model demonstrates an average precision nearly five times higher than random screening, highlighting the potential of ML in accelerating superconductor discovery. BETE-NET accelerates the search for high-$T_c$ superconductors while setting a precedent for applying ML in materials discovery, particularly when data is limited.

cond-mat.supr-con↗

arXiv2023

Charge State-Dependent Symmetry Breaking of Atomic Defects in Transition Metal Dichalcogenides

The functionality of atomic quantum emitters is intrinsically linked to their host lattice coordination. Structural distortions that spontaneously break the lattice symmetry strongly impact their optical emission properties and spin-photon interface. Here we report on the direct imaging of charge state-dependent symmetry breaking of two prototypical atomic quantum emitters in mono- and bilayer MoS$_2$ by scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM). By substrate chemical gating different charge states of sulfur vacancies (Vac$_\text{S}$) and substitutional rhenium dopants (Re$_\text{Mo}$) can be stabilized. Vac$_\text{S}^{-1}$ as well as Re$_\text{Mo}^{0}$ and Re$_\text{Mo}^{-1}$ exhibit local lattice distortions and symmetry-broken defect orbitals attributed to a Jahn-Teller effect (JTE) and pseudo-JTE, respectively. By mapping the electronic and geometric structure of single point defects, we disentangle the effects of spatial averaging, charge multistability, configurational dynamics, and external perturbations that often mask the presence of local symmetry breaking.

cond-mat.mtrl-sci↗

arXiv2023

Ultra-fast interpretable machine-learning potentials

All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long time scales.

cond-mat.mtrl-sci↗

arXiv2022

Magnetic molecule tunnel heterojunctions

We characterize molecular magnet heterojunctions in which sublimated CoPc films as thin as 5 nm are sandwiched between transparent conducting bottom-layer indium tin oxide and top-layer soft-landing eutectic GaIn (EGaIn) electrodes. The roughness of the cobalt phthalocyanine (CoPc) films was determined by atomic force microscopy to be on the order of several nanometers, and crystalline ordering of lying-down planar molecules was confirmed by X-ray diffraction. The current-voltage (I-V) characteristics reveal the onset of a superconducting gap at Tc = 6 K, which together with higher temperature fits to a modified Simmons' model, provide incontrovertible evidence for direct quantum mechanical tunneling processes through the magnetic molecules in our heterojunctions. The voltage dependent features in the differential conductance measurements relate to spin states of single molecules or aggregates of molecules and should prove to be important for quantum information device development.

cond-mat.mes-hall↗

arXiv2022

Data-Augmentation for Graph Neural Network Learning of the Relaxed Energies of Unrelaxed Structures

Computational materials discovery has continually grown in utility over the past decade due to advances in computing power and crystal structure prediction algorithms (CSPA). However, the computational cost of the \textit{ab initio} calculations required by CSPA limits its utility to small unit cells, reducing the compositional and structural space the algorithms can explore. Past studies have bypassed many unneeded \textit{ab initio} calculations by utilizing machine learning methods to predict formation energy and determine the stability of a material. Specifically, graph neural networks display high fidelity in predicting formation energy. Traditionally graph neural networks are trained on large data sets of relaxed structures. Unfortunately, the geometries of unrelaxed candidate structures produced by CSPA often deviate from the relaxed state, which leads to poor predictions hindering the model's ability to filter energetically unfavorable prior to \textit{ab initio} evaluation. This work shows that the prediction error on relaxed structures reduces as training progresses, while the prediction error on unrelaxed structures increases, suggesting an inverse correlation between relaxed and unrelaxed structure prediction accuracy. To remedy this behavior, we propose a simple, physically motivated, computationally cheap perturbation technique that augments training data to improve predictions on unrelaxed structures dramatically. On our test set consisting of 623 Nb-Sr-H hydride structures, we found that training a crystal graph convolutional neural networks, utilizing our augmentation method, reduced the MAE of formation energy prediction by 66\% compared to training with only relaxed structures. We then show how this error reduction can accelerates CSPA by improving the model's ability to filter out energetically unfavorable structures accurately.

physics.comp-ph↗

arXiv2021

Crystal Structure of the $τ_{11}$ Al$_4$Fe$_{1.7}$Si Phase from Neutron Diffraction and Ab Initio Calculations

The intermetallic $τ_{11}$ Al$_4$Fe$_{1.7}$Si phase is of interest for high-temperature structural application due to its combination of low density and high strength. We determine the crystal structure of the $τ_{11}$ phase through a combination of powder neutron diffraction and density functional theory calculations. Using Pawley and Rietveld refinements of the neutron diffraction data provides an initial crystal structure model. Since Al and Si have nearly identical neutron scattering lengths, we use density-functional calculations to determine their preferred site occupations. The $τ_{11}$ phase exhibits a hexagonal crystal structure with space group $P6_3/mmc$ and lattice parameters of $a=7.478$~Å and $c=7.472$~Å. The structure comprises five Wyckoff positions; Al occupies the $6h$ and $12k$ sites, Fe the $2a$ and $6h$ sites, and Si the $2a$ sites. We observe site disorder and partial occupancies on all sites with a large fraction of 80\% Fe vacancies on the $2d$ sites, indicating an entropic stabilization of the $τ_{11}$ phase at high temperature.

cond-mat.mtrl-sci↗

arXiv2021

Controllable p-type Doping of 2D WSe2 via Vanadium Substitution

Scalable substitutional doping of two-dimensional (2D) transition metal dichalcogenides (TMDCs) is a prerequisite to developing next-generation logic and memory devices based on 2D materials. To date, doping efforts are still nascent. Here, we report scalable growth and vanadium (V) doping of 2D WSe2 at front-end-of-line (FEOL) and back-end-of-line (BEOL) compatible temperatures of 800 °C and 400 °C, respectively. A combination of experimental and theoretical studies confirm that vanadium atoms substitutionally replace tungsten in WSe2, which results in p-type doping via the introduction of discrete defect levels that lie close to the valence band maxima. The p-type nature of the V dopants is further verified by constructed field-effect transistors, where hole conduction becomes dominant with increasing vanadium concentration. Hence, our study presents a method to precisely control the density of intentionally introduced impurities, which is indispensable in the production of electronic-grade wafer-scale extrinsic 2D semiconductors.

cond-mat.mtrl-sci↗

arXiv2021

Charged Vacancy Defects in Black Phosphorus Monolayer Phosphorene

The two-dimensional semiconductor phosphorene has attracted extensive research interests for potential applications in optoelectronics, spintronics, catalysis, sensors, and energy conversion. To harness phosphorene's potential requires a better understanding of how intrinsic defects control carrier concentration, character, and mobility. Using density-functional theory and a charge correction scheme to account for the appropriate boundary conditions, we conduct a comprehensive study of the effect of structure on the formation energy, electronic structure, and charge transition level of the charged vacancy point defects in phosphorene. We predict that the neutral vacancy exhibits a 9-5 ring structure with a formation energy of 1.7 eV and transitions to a negatively charged state at a Fermi level 1.04 eV above the valence band maximum. The corresponding optical charge transitions display sizeable Frank-Condon shifts with a large Stokes shift of 0.3 eV. Phosphorene vacancies should become negatively charged in n-doped phosphorene, which would passivate the dopants and reduce the charge carrier concentration and mobility.

cond-mat.mtrl-sci↗

arXiv2021

Superconductivity of $β$-Gallium

Elemental gallium can exist in several phases under ambient conditions. The stable $α$ phase has a superconducting transition temperature, $T_c$, of 0.9~K. By contrast, the $T_c$ of the metastable $β$ phase is around 6~K. To understand the significant improvement in $T_c$ in the $β$ phase, we first calculate the electronic structure, phonon dispersion, and the electron-phonon coupling of gallium in the $α$ and $β$ phase. Next, we solve the Eliashberg equations to obtain the superconducting gaps and the transition temperatures. Using these results, we relate the increased $T_c$ in the $β$ phase to structural differences between the phases that affect the electronic and phonon properties. The structure motif of the $α$ phase is Ga$_2$ dimers, which form strong covalent bonds leading to bonding and antibonding states that suppress the density of states at the Fermi level. The $β$-Ga structure consists of arrays of Ga chains that favor strong coupling between the lattice vibrations and the electronic states near the Fermi level. The increased density of states and strong coupling to the phonons for the $β$-Ga chains compared to the $α$ Ga$_2$ dimers enhance superconductivity in the $β$-Ga phase.

cond-mat.supr-con↗

arXiv2021

Computational Synthesis of Substrates by Crystal Cleavage

The discovery of novel substrate materials has been dominated by trial and error, opening the opportunity for a systematic search. To identify stable crystal surfaces, we generate bonding networks for materials from the Materials Project database with one to five atoms in the primitive unit cell. For three-dimensional crystals in this set, we systematically break up to three bonds in the bonding network of the primitive cell. Successful cleavage reduces the bonding network to two periodic dimensions, creating a layer of the cleaved crystal. We identify 4,693 unique cleavage surfaces across 2,133 bulk crystals, 4,626 of which have a maximum Miller index of 1. To characterize the likelihood of cleavage and the thermodynamic stability of the cleaved surfaces, we create monolayers of these surfaces and calculate the work of cleavage and the partially-relaxed surface energy using density functional theory to discover 3,991 potential substrates, 2,307 of which do not contain f-valence electrons and 2,183 of which are derived from a bulk precursor with an entry in the Inorganic Crystal Structure Database. Following, we identify distinct trends in the work of cleavage of these layers and relate them to metallic and covalent/ionic bonding of the three-dimensional precursor. We also assembled a database of commercially available substrates and show that the database of predicted substrates significantly enhances the diversity and range of the distribution of electronic properties and lattice parameters, providing opportunities for the epitaxial growth of many materials. We illustrate the potential impact of the substrate database by identifying several new epitaxial substrates for the transparent conductor BaSnO3, which exhibit low cleavage energies and result in strains an order of magnitude lower than currently used substrates.

cond-mat.mtrl-sci↗

arXiv2020

Stability of charged sulfur vacancies in 2D and bulk MoS$_2$ from plane-wave density functional theory with electrostatic corrections

Two-dimensional (2D) semiconducting transition metal dichalcogenides such as MoS$_2$ have attracted extensive research interests for potential applications in optoelectronics, spintronics, photovoltaics, and catalysis. To harness the potential of these materials for electronic devices requires a better understanding of how defects control the carrier concentration, character, and mobility. Utilizing a correction scheme developed by Freysoldt and Neugebauer to ensure the appropriate electrostatic boundary conditions for charged defects in 2D materials, we perform density functional theory calculations to compute formation energies and charge transition levels associated with sulfur vacancies in monolayer and layered bulk MoS$_2$. We investigate the convergence of these defect properties with respect to vacuum spacing, in-plane supercell dimensions, and different levels of theory. We also analyze the electronic structures of the defects in different charge states to gain insights into the effect of defects on bonding and magnetism. We predict that both vacancy structures undergo a Jahn-Teller distortion, which helps stabilize the sulfur vacancy in the $-1$ charged state.

cond-mat.mtrl-sci↗

arXiv2020

Uncertainty Quantification for Materials Properties in Density Functional Theory with k-Point Density

Many computational databases emerged over the last five years that report material properties calculated with density functional theory. The properties in these databases are commonly calculated to a precision that is set by choice of the basis set and the k-point density for the Brillouin zone integration. We determine how the precision of properties obtained from the Birch equation of state for 29 transition metals and aluminum in the three common structures -- fcc, bcc, and hcp -- correlate with the k-point density and the precision of the energy. We show that the precision of the equilibrium volume, bulk modulus, and the pressure derivative of the bulk modulus correlate comparably well with the k-point density and the precision of the energy, following an approximate power law. We recommend the k-point density as the convergence parameter because it is computationally efficient, easy to use as a direct input parameter, and correlates with property precision at least as well as the energy precision. We predict that common k-point density choices in high throughput DFT databases result in precision for the volume of 0.1%, the bulk modulus of 1%, and the pressure derivative of 10%.

cond-mat.mtrl-sci↗

arXiv2019

Implicit self-consistent electrolyte model in plane-wave density-functional theory

The ab-initio computational treatment of electrochemical systems requires an appropriate treatment of the solid/liquid interfaces. A fully quantum mechanical treatment of the interface is computationally demanding due to the large number of degrees of freedom involved. In this work, we describe a computationally efficient model where the electrode part of the interface is described at the density-functional theory (DFT) level, and the electrolyte part is represented through an implicit solvation model based on the Poisson-Boltzmann equation. We describe the implementation of the linearized Poisson-Boltzmann equation into the Vienna Ab-initio Simulation Package (VASP), a widely used DFT code, followed by validation and benchmarking of the method. To demonstrate the utility of the implicit electrolyte model, we apply it to study the surface energy of Cu crystal facets in an aqueous electrolyte as a function of applied electric potential. We show that the applied potential enables the control of the shape of nanocrystals from an octahedral to a truncated octahedral morphology with increasing potential.

cond-mat.mtrl-sci↗

arXiv2017

Topology-Scaling Identification of Layered Solids and Stable Exfoliated 2D Materials

The Materials Project crystal structure database has been searched for materials possessing layered motifs in their crystal structures using a topology-scaling algorithm. The algorithm identifies and measures the sizes of bonded atomic clusters in a structure's unit cell, and determines their scaling with cell size. The search yielded 826 stable layered materials, which are considered as candidates for the formation of two-dimensional monolayers via exfoliation. Density-functional theory calculates the exfoliation energy of each material and 681 monolayers are found to exhibit exfoliation energies below those of certain already-extant two-dimensional materials, indicating the possibility of exfoliating them from bulk phases. The crystal structures of these two-dimensional materials provide templates for future theoretical searches of stable two-dimensional materials. The optimized structures and other data for all 826 monolayers are provided at https://materialsweb.org .

cond-mat.mtrl-sci↗

arXiv2017

The conundrum of relaxation volumes in first-principles calculations of charge defects

The defect relaxation volumes obtained from density-functional theory (DFT) calculations of charged vacancies and interstitials are much larger than their neutral counterparts, seemingly unphysically large. In this work, we investigate the possible reasons for this and revisit the methods that address the calculation of charged defect structures in periodic DFT. We probe the dependence of the proposed energy corrections to charged defect formation energies on relaxation volumes and find that corrections such as the image charge correction and the alignment correction, which can lead to sizable changes in defect formation energies, have an almost negligible effect on the charged defect relaxation volume. We also investigate the volume for the net neutral defect reactions comprised of individual charged defects, and find that the aggregate formation volumes have reasonable magnitudes. This work highlights an important issue that, as for defect formation energies, the defect formation volumes depend on the choice of reservoir. We show that considering the change in volume of the electron reservoir in the formation reaction of the charged defects, analogous to how volumes of atoms are accounted for in defect formation volumes, can renormalize the formation volumes of charged defects such that they are comparable to neutral defects. This approach enables the description of the elastic properties of isolated charged defects within the overall neutral material, beyond the context of the overall defect reactions that produce the charged defect.

cond-mat.mtrl-sci↗

arXiv2016

MPInterfaces: A Materials Project based Python Tool for High-Throughput Computational Screening of Interfacial Systems

A Materials Project based open-source Python tool, MPInterfaces, has been developed to automate the high-throughput computational screening and study of interfacial systems. The framework encompasses creation and manipulation of interface structures for solid/solid hetero-structures, solid/implicit solvents systems, nanoparticle/ligands systems; and the creation of simple system-agnostic workflows for in depth computational analysis using density-functional theory or empirical energy models. The package leverages existing open-source high-throughput tools and extends their capabilities towards the understanding of interfacial systems. We describe the various algorithms and methods implemented in the package. Using several test cases, we demonstrate how the package enables high-throughput computational screening of advanced materials, directly contributing to the Materials Genome Initiative (MGI), which aims to accelerate the discovery, development, and deployment of new materials.

cond-mat.mtrl-sci↗

arXiv2014

Comparison of polynomial approximations to speed up planewave-based quantum Monte Carlo calculations

The computational cost of quantum Monte Carlo (QMC) calculations of realistic periodic systems depends strongly on the method of storing and evaluating the many-particle wave function. Previous work [A. J. Williamson et al., Phys. Rev. Lett. 87, 246406 (2001); D. Alfè and M. J. Gillan, Phys. Rev. B 70, 161101 (2004)] has demonstrated the reduction of the O(N^3) cost of evaluating the Slater determinant with planewaves to O(N^2) using localized basis functions. We compare four polynomial approximations as basis functions -- interpolating Lagrange polynomials, interpolating piecewise-polynomial-form (pp-) splines, and basis-form (B-) splines (interpolating and smoothing). All these basis functions provide a similar speedup relative to the planewave basis. The pp-splines have eight times the memory requirement of the other methods. To test the accuracy of the basis functions, we apply them to the ground state structures of Si, Al, and MgO. The polynomial approximations differ in accuracy most strongly for MgO and smoothing B-splines most closely reproduce the planewave value for of the variational Monte Carlo energy. Using separate approximations for the Laplacian of the orbitals increases the accuracy sufficiently to justify the increased memory requirement, making smoothing B-splines, with separate approximation for the Laplacian, the preferred choice for approximating planewave-represented orbitals in QMC calculations.

physics.comp-ph↗