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Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials
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Phonon-engineered extreme thermal conductivity materials
Materials with ultrahigh or low thermal conductivity are desirable for many technological applications, such as thermal management of electronic and photonic devices, heat exchangers, energy converters and thermal insulation. Recent advances in simulation tools (first principles, the atomistic Green’s function and molecular dynamics) and experimental techniques (pump–probe techniques and microfabricated platforms) have led to new insights on phonon transport and scattering in materials and the discovery of new thermal materials, and are enabling the engineering of phonons towards desired thermal properties. Here, we review recent discoveries of both inorganic and organic materials with ultrahigh and low thermal conductivity, highlighting heat-conduction physics, strategies used to change thermal conductivity, and future directions to achieve extreme thermal conductivities in solid-state materials.
Simulation of the photodetachment spectra of the nitrate anion (NO 3 ₋ ) in the B ~ 2 E' energy range and non-adiabatic electronic population dynamics of NO 3
The photodetachment spectrum of the nitrate anion (NO 3 ₋ ) in the energy range of the NO 3 second excited state is simulated from first principles using quantum wave packet dynamics. The prediction at 10 K and 435 K relies on the use of an accurate full-dimensional fully coupled five state diabatic potential model utilizing an artificial neural network. The ability of this model to reproduce experimental spectra was demonstrated recently for the lower energy range. Analysis of the spectra indicates a weaker Jahn–Teller coupling compared to the first excited state. The detailed non-adiabatic dynamics is studied by computing the population dynamics. An ultra-fast non-statistical radiationless decay is found only among the Jahn–Teller components, which is followed by a slow statistical non-radiative decay among the different state manifolds. The latter is reproduced perfectly by a simple first order kinetics model. The dynamics in the second excited state is not affected by the presence of a conical intersection with the first excited state manifold.
On the collisional damping of plasma velocity space instabilities
For plasma velocity space instabilities driven by particle distributions significantly deviated from a Maxwellian, weak collisions can damp the instabilities by an amount that is significantly beyond the collisional rate itself. This is attributed to the dual role of collisions that tend to relax the plasma distribution toward a Maxwellian and to suppress the linearly perturbed distribution function. The former effect can dominate in cases where the unstable non-Maxwellian distribution is driven by collisionless transport on a timescale much shorter than that of collisions, and the growth rate of the ideal instability has a sensitive dependence on the distribution function. The whistler instability driven by electrostatically trapped electrons is used as an example to elucidate such a strong collisional damping effect of plasma velocity space instabilities, which is confirmed by first-principles kinetic simulations.
Spin-textured Chern bands in AB-stacked transition metal dichalcogenide bilayers
Significance The long-wavelength moiré superlattices in twisted two-dimensional (2D) structures have emerged as a highly tunable platform for strongly correlated electron physics. In this work, we reveal the displacement field–induced topological moiré bands in AB-stacked transition-metal dichalcogenide (TMD) heterobilayer M o T e 2 / W S e 2 . Valley-contrasting Chern bands with nontrivial spin texture are formed from interlayer hybridization between M o T e 2 and W S e 2 bands of nominally opposite spins. The general mechanism for band inversion we uncovered in AB-stacked TMD bilayers is quite robust and can be applied to other material combinations as well. Our study establishes a recipe for creating topological bands in AB-stacked TMD bilayers in general, which provides a highly tunable platform for realizing quantum-spin Hall and interaction-induced quantum anomalous Hall effects.
Ab Initio modeling of Near-Edge EELS spectra for chemisorbed molecules
Abstract Electron energy loss spectroscopy (EELS) has recently been applied to probe chemisorbed molecules on metal nanostructures, but a fundamental understanding of the correlation between these spectra and the electronic structures of the adsorbates has been limited. We report here on the insights afforded by time-dependent density functional theory to decipher the energy loss near edge structure (ELNES) of EELS spectra associated with chemisorption. These first-principles calculations simulate the ELNES-EELS spectra for chemisorbed CO on various facets of Au and Pt. Computational predictions of key signatures such as the ‘red shift’ and reductions in the peak intensity of the 2 π * and 6 σ * peaks, as compared to free CO in the gas phase, are validated in comparison to experimentally collected EELS spectra. These signatures are revealed to arise from changes in the electronic structure in terms of unoccupied density of states associated with the chemisorption process. They are consistent with a Blyholder model that incorporates donation and back-donation of electrons. They are also characteristic of the chemisorption process, such as the choice of metal, site of adsorption and the coverage and distribution of adsorbates. Our simulations thus provide guidelines for the use of ELNES-EELS to characterize the atomic structure and adsorption property of nanostructured surfaces and facilitate the development of advanced nanomaterials for catalytic applications.
Heat transport in liquid water from first-principles and deep neural network simulations
In this work, we compute the thermal conductivity of water within linear response theory from equilibrium molecular dynamics simulations, by adopting two different approaches. In one, the potential energy surface (PES) is derived on the fly from the electronic ground state of density functional theory (DFT) and the corresponding analytical expression is used for the energy flux. In the other, the PES is represented by a deep neural network (DNN) trained on DFT data, whereby the PES has an explicit local decomposition and the energy flux takes a particularly simple expression. By virtue of a gauge invariance principle, established by Marcolongo, Umari, and Baroni, the two approaches should be equivalent if the PES were reproduced accurately by the DNN model. We test this hypothesis by calculating the thermal conductivity, at the GGA (PBE) level of theory, using the direct formulation and its DNN proxy, finding that both approaches yield the same conductivity, in excess of the experimental value by approximately 60%. Besides being numerically much more efficient than its direct DFT counterpart, the DNN scheme has the advantage of being easily applicable to more sophisticated DFT approximations, such as meta-GGA and hybrid functionals, for which it would be hard to derive analytically the expression of the energy flux. We find in this way that a DNN model, trained on meta-GGA (SCAN) data, reduces the deviation from experiment of the predicted thermal conductivity by about 50%, leaving the question open as to whether the residual error is due to deficiencies of the functional, to a neglect of nuclear quantum effects in the atomic dynamics, or, likely, to a combination of the two.
Possible Kitaev Quantum Spin Liquid State in 2D Materials with S=3/2
Quantum spin liquids (QSLs) form an extremely unusual magnetic state in which the spins are highly correlated and fluctuate coherently down to the lowest temperatures, but without symmetry breaking and without the formation of any static long-range-ordered magnetism. Such intriguing phenomena are not only of great fundamental relevance in themselves, but also hold promise for quantum computing and quantum information. Further, among different types of QSLs, the exactly solvable Kitaev model is attracting much attention, with most proposed candidate materials, e.g., RuCl 3 and Na 2 IrO 3 , having an effective $\textit{S}$ = 1/2 spin value. Here, via extensive first-principles-based simulations, we report the investigation of the Kitaev physics and possible Kitaev QSL state in epitaxially strained Cr-based monolayers, such as CrSiTe 3 , that rather possess a $\textit{S}$ = 3/2 spin value. Our study thus extends the playground of Kitaev physics and QSLs to 3$\textit{d}$ transition metal compounds.
Coherent Electromagnetic Emission from Relativistic Magnetized Shocks
Relativistic magnetized shocks are a natural source of coherent emission, offering a plausible radiative mechanism for fast radio bursts (FRBs). We present first-principles 3D simulations that provide essential information for the FRB models based on shocks: the emission efficiency, spectrum, and polarization. The simulated shock propagates in an e ± plasma with magnetization σ > 1 . The measured fraction of shock energy converted to coherent radiation is ≃ 10 -3 σ -1 , and the energy-carrying wave number of the wave spectrum is ≃ 4ω c /c, where ω c is the upstream gyrofrequency. The ratio of the O-mode and X-mode energy fluxes emitted by the shock is ≃ 0.4σ -1 . The dominance of the X mode at σ >> 1 is particularly strong, approaching 100% in the spectral band around 2ω c . We also provide a detailed description of the emission mechanism for both X and O modes.
Muonium hydride: The lowest density crystal
A muonium hydride molecule is a bound state of muonium and hydrogen atoms. It has half the mass of a parahydrogen molecule and very similar electronic properties in its ground state. The phase diagram of an assembly of such particles is investigated by first-principles quantum simulations. In the bulk limit, the low-temperature equilibrium phase is a crystal of extraordinarily low density, lower than that of any other known atomic or molecular crystal. Despite the low density and particle mass, the melting temperature is surprisingly high (close to 9 K). No (metastable) supersolid phase is observed. We investigated the physical properties of nanoscale clusters (up to 200 particles) of muonium hydride and found the superfluid response to be greatly enhanced compared to that of parahydrogen clusters. The possible experimental realization of these systems is discussed.
Quasiparticle Couplings in Transport of Heat, Charge, and Spin for Novel Energy Materials. Final report
Understanding the microscopic processes involved in the transport and conversion of energy from the atomic-scale to the meso-scale is critical for the development of next-generation materials for energy conversion and information processing. The goal of this program was to elucidate the couplings between microscopic degrees-of-freedom of atomic vibrations, spins, and electrons, which underpin heat, charge, and spin transport. This project systematically investigated these quasi-particle interactions, using state-of-the-art neutron scattering techniques, combined with first-principles computer simulations. This research program uncovered new information and understanding, which helps to rationalize materials properties based on the interplay of their atomistic degrees of freedom.
Towards Realistic Models of Heterogeneous Catalysis: Simulations of Oxidation Catalysis from First Principles
Among the most significant developments in heterogeneous catalysis in the last 20 years is the emergence of microkinetic models, often parameterized from density functional theory (DFT) calculations, used to quantify observed catalyst performance and to guide the discovery of new catalytic materials. While DFT directly reports binding energies and elementary step activation energies, the free energies that enter into microkinetic models must be computed from additional approximations. Frequently these free energy approximations assume ideal behavior—that adsorbates do not interact with one another, or that adsorbates are immobile or vibrate harmonically about a binding site. These assumptions can and do have an impact on predicted catalyst performance and potentially even on predicted trends. Our work relates to three categories of non-idealities, explored in the context of nitrogen oxidation and reduction catalysis on metal surfaces. One component relates to adsorbate translational free energy, the contribution least well described by conventional models, in which we develop modeling approaches that improve accuracy with limited increase in computational expense. A second component relates adsorbate-adsorbate interactions, in which we develop benchmark on-lattice interaction models and compare kinetic predictions with conventional mean-field models, in an effort to develop more robust coverage-dependent mean field modeling approaches. The last relates the most fundamental assumption of all—that of energy equipartition—in an effort to rationalize and guide plasma-enhanced catalysis.
Advanced Modeling of Beam Physics and Performance Optimization for Nuclear Physics Colliders
High energy colliders provide a critical tool in nuclear physics study by probing the fundamental structure and dynamics of matter. To maximize the potential of scientific discovery in nuclear physics study, it is important to optimize the parameters of these colliders to attain the best performance. The performance of a collider is typically measured by its integrated luminosity of colliding beams since the probability of a new event is proportional to the integrated luminosity. However, the achievable luminosity is limited by the electromagnetic interactions (beam-beam effects) of two colliding beams at higher energy, and the interplay between the space-charge effects and the beam-beam effects at lower energy. To achieve the best performance of a collider means to attain the highest luminosity of the collider with optimized collider parameters. Optimizing the collider’s machine parameters is both computationally and experimentally expensive. A fast and robust computational framework including beam-beam and space-charge effects will be critical to attaining the best performance of the collider. In this project, we will study the beam dynamics challenges, specifically the interplay of the space-charge and the beam-beam effects, and the machine tuning models for maximizing the performance of RHIC experiments. We will develop an advanced modeling framework based on first-principles physical simulations, lattice models and the state-of-the-art machine learning methods and apply this framework to performance improvement of the RHIC in operation. We will build data manipulation packages to connect the simulation data and the experimental data with the framework, develop a self-consistent hybrid model of space-charge and beam-beam effects, study underlying physics mechanisms, build surrogate models using the labeled data, integrate the models into the advanced modeling framework, and apply the framework to RHIC luminosity (STAR and sPHENIX) optimization. The success of this project would substantially improve the performance of existing and future colliders and increase the opportunity for scientific discovery.
Understanding and Predicting Pedestal Scenarios on NSTX-U (Final Technical Report)
This project investigated gyrokinetic instabilities in the NSTX pedestal, identified the major transport mechanisms (some of them novel) in the NSTX pedestal, and developed and validated a predictive modeling capability for pedestal transport in spherical tokamaks, with particular emphasis on NSTX discharges. The work combined first-principles gyrokinetic simulations, reduced transport models, integrated transport calculations with ASTRA, and exploratory machine learning tools. The central outcome is a practical modeling capability for pedestal temperature profiles based on reduced models informed and constrained by gyrokinetic physics.
Collaborative Research: Enabling multi-scale studies of magnetic reconnection with interpretable data-driven models
The development of accurate reduced descriptions and improved closures for magnetic reconnection is an important and a long‐standing challenge in plasma physics. The four‐fluid approach, and associated closures, that were investigated have the potential to improve the accuracy of plasma fluid models, capturing physical effects which would otherwise require a kinetic description. If successful, this approach could have an important impact for the modeling of laboratory and space plasmas. The major goals of this project were to develop new machine learning (ML) tools based on sparse and symbolic regression techniques, and to extract interpretable and generalizable reduced models (e.g., in the form of partial differential equations - PDEs) from data generated by first principles plasma simulations. Preserving interpretability of such data‐driven models is key to addressing the long‐standing theoretical and numerical challenges. Prior proof‐of‐principle studies have demonstrated the enormous potential of this approach, by recovering the well‐established hierarchy of plasma equations (from Vlasov to MHD) from data produced by particle‐in‐cell (PIC) simulations. Our goal in this project was to extend and apply these new tools to construct better kinetic closures for magnetic reconnection; to derive better models of particle injection and acceleration by this fundamental plasma process; and to use this understanding to accelerate the development of multi‐scale plasma algorithms. While our immediate focus was on the problem of magnetic reconnection, the tools that were will developed are general and applicable to other areas of plasma physics, and more broadly to many‐body phenomena. We anticipate that the development of these multi‐scale models will have a significant impact across different areas of plasma science, from fusion to space and astrophysical plasmas.
Exploring Mixed Ionic–Electronic-Conducting PVA/PEDOT:PSS Hydrogels as Channel Materials for Organic Electrochemical Transistors
Here, we report the preparation and evaluation of PVA/PEDOT:PSS-conducting hydrogels working as channel materials for OECT applications, focusing on the understanding of their charge transport and transfer properties. Our conducting hydrogels are based on crosslinked PVA with PEDOT:PSS interacting via hydrogen bonding and exhibit an excellent swelling ratio of ~180–200% w/w. Our electrochemical impedance studies indicate that the charge transport and transfer processes at the channel material based on conducting hydrogels are not trivial compared to conducting polymeric films. The most relevant feature is that the ionic transport through the swollen hydrogel is clearly different from the transport through the solution, and the charge transfer and diffusion processes govern the low-frequency regime. In addition, we have performed in operando Raman spectroscopy analyses in the OECT devices supported by first-principle computational simulations corroborating the doping/de-doping processes under different applied gate voltages. The maximum transconductance (gm~1.05 μS) and maximum volumetric capacitance (C*~2.3 F.cm -3 ) values indicate that these conducting hydrogels can be promising candidates as channel materials for OECT devices.
Relativistic Alfvén Waves Entering Charge-starvation in the Magnetospheres of Neutron Stars
Instabilities in a neutron star can generate Alfvén waves in its magnetosphere. Propagation along the curved magnetic field lines strongly shears the wave, boosting its electric current j A . We derive an analytic expression for the evolution of the wavevector k and the growth of j A . In the strongly sheared regime, j A may exceed the maximum current j0 that can be supported by the background e ± plasma. We investigate these charge-starved waves, first using a simplified two-fluid analytic model, then with first-principles kinetic simulations. We find that the Alfvén wave is able to propagate successfully even when κ ≡ j A /j 0 $\ll$ 1. It sustains j A by compressing and advecting the plasma along the magnetic field lines with an increasing Lorentz factor, γ ≳ κ 1/2 . The simulations show how plasma instabilities lead to gradual dissipation of the wave energy. Our results suggest that an extremely high charge-starvation parameter κ ≳ 10 4 may be required in order for this mechanism to power the observed fast radio bursts (FRBs) from SGR 1935+2154. However, cosmological FRBs with much higher luminosities are unlikely to be a result of charge-starvation.