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At least 919 records · Page 51

Bipartite mutual information in classical many-body dynamics

Information theoretic measures have helped to sharpen our understanding of many-body quantum states. As perhaps the most well-known example, the entanglement entropy (or more generally, the bipartite mutual information) has become a powerful tool for characterizing the dynamical growth of quantum correlations. By contrast, although computable, the bipartite mutual information (MI) is almost never explored in classical many particle systems; this owes in part to the fact that computing the MI requires keeping track of the evolution of the full probability distribution, a feat which is rarely done (or thought to be needed) in classical many-body simulations. Here, we utilize the MI to analyze the spreading of information in 1D elementary cellular automata (CA). Broadly speaking, we find that the behavior of the MI in these dynamical systems exhibits a few different types of scaling that roughly correspond to known CA universality classes. Of particular note is that we observe a set of automata for which the MI converges parametrically slowly to its thermodynamic value. We develop a microscopic understanding of this behavior by analyzing a two-species model of annihilating particles moving in opposite directions. Furthermore, our work suggests the possibility that information theoretic tools such as the MI might enable a more fine-grained characterization of classical many-body states and dynamics.

Cellular automata↗

Lanczos Algorithm, the Transfer Matrix, and the Signal-to-Noise Problem

This Letter introduces a method for determining the energy spectrum of lattice quantum chromodynamics by applying the Lanczos algorithm to the transfer matrix and using a bootstrap generalization of the Cullum-Willoughby method to filter out spurious eigenvalues. Proof-of-principle analyses of the simple harmonic oscillator and the lattice quantum chromodynamics proton mass demonstrate that this method provides faster ground-state convergence than the “effective mass,” which is related to the power-iteration algorithm. Lanczos provides more accurate energy estimates than multistate fits to correlation functions with small imaginary times while achieving comparable statistical precision. Two-sided error bounds are computed for Lanczos results and guarantee that excited-state effects cannot shift Lanczos results far outside their statistical uncertainties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Influence of Real Gas Effects on Chemical Kinetics in Oxycombustion in Supercritical Carbon Dioxide

Oxycombustion in supercritical carbon dioxide is an integral part of the Allam Cycle, a technology that enables carbon-neutral use of fossil-fuels and carbon-negative use of biofuels. We simulate oxycombustion in a realistic combustor geometry for two sets of fuel conditions: pure methane, and a 40 percent methane/60 percent carbon dioxide blend, both at 343.15 K. The fuel jet mixes with a preheated swirler of 20 percent oxygen and 80 percent carbon dioxide at 1005.35 K, with a 100 percent carbon dioxide coflow at 783.15 K. The entire system operates at a pressure of 300 bar, putting the entire system above the critical temperature and pressure of carbon dioxide. Simulations are performed using PeleC, a compressible block-structured adaptive-mesh refinement (AMR) reacting flow code. Direct comparison of results using thermodynamically self-consistent implementations of the ideal gas equation of state and the Soave-Redlich-Kwong (SRK) equation of state allow quantification of the real gas impacts on combustion kinetics, flow temperatures, and pollutant formation.

carbon dioxide↗

Structure, Stability, and Electronic Properties of Boron Suboxide: A Density Functional Theory Study

Boron suboxide (B 6 O) is a boron-rich compound derived from the α-rhombohedral boron lattice with extreme hardness and unusual semiconducting properties. In this work, density functional theory (DFT) was used to show that unit cell volume, mechanical strength, band gaps, and thermodynamic stabilities of B 6 O were influenced by the interstitial elements and point defects at the icosahedral sites. While the hexagonal unit cell volume (HUCV) varies with interstitial occupancy, it is the icosahedral defect that weakens the intrinsic bulk modulus of B 6 O. Using the hybrid HSE functional, we confirmed that the perfect B 6 O bulk is a p-type semiconductor with a direct band gap of 2.8 eV. Furthermore, by screening α-boron compounds systematically, we found that a simple octet rule may offer a consistent explanation for the variations in the computed electronic structures. The formation free energies calculated over a wide range of temperatures (0–2500 K) and pressures (0–80 GPa) predict that formations of interstitial defects become favorable only at higher temperatures (ca. 1800 K) in bulk B 6 O lattices. The nudged elastic band (NEB) method was employed to identify the minimum energy pathways for the diffusions of dislocated B and O atoms. The diffusion of icosahedral B atoms has an energy barrier of 0.16 eV. More complex B diffusion paths involving the reorganization of icosahedral boron atoms incur higher barriers (>1 eV). In contrast, the diffusion of interstitial O atoms is facile with a barrier of 0.4 eV. Lastly, successive O insertions into the α-B lattice were performed using DFT to generate a basic understanding of the oxidation process. Furthermore, these calculations provide fundamental atomistic insights into the growth of B 6 O crystals and control of their point defects.

36 MATERIALS SCIENCE↗

Transport coefficients of the quark-gluon plasma at the critical point and across the first-order line

A bottom-up Einstein-Maxwell-dilaton holographic model is used to compute, for the first time, the behavior of several transport coefficients of the hot and baryon-rich strongly coupled quark-gluon plasma at the critical point and also across the first-order phase transition line in the phase diagram. The observables under study are the shear and bulk viscosities, the baryon and thermal conductivities, the baryon diffusion, the jet quenching parameter $\hat{q}$, as well as the heavy-quark drag force and the Langevin diffusion coefficients. These calculations provide a phenomenologically promising estimate for these coefficients, given that our model quantitatively reproduces lattice QCD thermodynamics results, both at zero and finite baryon density, besides naturally incorporating the nearly perfect fluidity of the quark-gluon plasma. Here we find that the diffusion of baryon charge, and also the shear and bulk viscosities, are suppressed with increasing baryon density, indicating that the medium becomes even closer to perfect fluidity at large densities. On the other hand, the jet quenching parameter and the heavy-quark momentum diffusion are enhanced with increasing density. The observables display a discontinuity gap when crossing the first-order phase transition line, while developing an infinite slope at the critical point. The transition temperatures associated with different transport coefficients differ in the crossover region but are found to converge at the critical point.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

BattPhase—A Convergent, Non-Oscillatory, Efficient Algorithm and Code for Predicting Shape Changes in Lithium Metal Batteries Using Phase-Field Models: Part I. Secondary Current Distribution

Electrochemical models at different scales and varying levels of complexity have been used in the literature to study the evolution of the anode surface in lithium metal batteries. This includes continuum, mesoscale (phase-field approaches), and multiscale models. Thermodynamics-based equations have been used to study phase changes in lithium batteries using phase-field approaches. However, grid convergence studies and the effect of additional parameters needed to simulate these models are not well-documented in the literature. In this paper, using a motivating example of a moving boundary model in one- and two-dimensions, we show how one can formulate phase-field models, implement algorithms for the same and analyze the results. An open-access code with no restrictions is provided as well. This article concludes with some thoughts on the computational efficiency of phase-field models for simulating dendritic growth.

25 ENERGY STORAGE↗

Simulating Effective QED on Quantum Computers

In recent years simulations of chemistry and condensed materials has emerged as one of the preeminent applications of quantum computing, offering an exponential speedup for the solution of the electronic structure for certain strongly correlated electronic systems. To date, most treatments have ignored the question of whether relativistic effects, which are described most generally by quantum electrodynamics (QED), can also be simulated on a quantum computer in polynomial time. Here we show that effective QED, which is equivalent to QED to second order in perturbation theory, can be simulated in polynomial time under reasonable assumptions while properly treating all four components of the wavefunction of the fermionic field. In particular, we provide a detailed analysis of such simulations in position and momentum basis using Trotter-Suzuki formulas. We find that the number of T -gates needed to perform such simulations on a 3 D lattice of n s sites scales at worst as O ( n s 3 / ϵ ) 1 + o ( 1 ) in the thermodynamic limit for position basis simulations and O ( n s 4 + 2 / 3 / ϵ ) 1 + o ( 1 ) in momentum basis. We also find that qubitization scales slightly better with a worst case scaling of O ~ ( n s 2 + 2 / 3 / ϵ ) for lattice eQED and complications in the prepare circuit leads to a slightly worse scaling in momentum basis of O ~ ( n s 5 + 2 / 3 / ϵ ) . We further provide concrete gate counts for simulating a relativistic version of the uniform electron gas that show challenging problems can be simulated using fewer than 10 13 non-Clifford operations and also provide a detailed discussion of how to prepare multi-reference configuration interaction states in effective QED which can provide a reasonable initial guess for the ground state. Finally, we estimate the planewave cutoffs needed to accurately simulate heavy elements such as gold.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Engineered Nanoporous Frameworks for Adsorption Cooling Applications

The energy demand for traditional vapor-compressed technology for space cooling continues to soar year after year due to global warming and the increasing human population’s need to improve living and working conditions. Thus, there is a growing demand for eco-friendly technologies that use sustainable or waste energy resources. Here this review discusses the properties of various refrigerants used for adsorption cooling applications followed by a brief discussion on the thermodynamic cycle. Next, sorbents traditionally used for cooling are reviewed to emphasize the need for advanced capture materials with superior properties to improve refrigerant sorption. The remainder of the review focus on studies using engineered nanoporous frameworks (ENFs) with various refrigerants for adsorption cooling applications. The effects of the various factors that play a role in ENF–refrigerant pair selection, including pore structure/dimension/shape, morphology, open-metal sites, pore chemistry and possible presence of defects, are reviewed. Next, in-depth insights into the sorbent–refrigerant interaction, and pore filling mechanism gained through a combination of characterization techniques and computational modeling are discussed. Finally, we outline the challenges and opportunities related to using ENFs for adsorption cooling applications and provide our views on the future of this technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Liquid Structure and Transport Properties of the Deep Eutectic Solvent Ethaline

A range of techniques including physical property measurements, neutron scattering experiments, ab initio molecular dynamics, and classical molecular dynamics simulations are used to probe the structural, thermodynamic, and transport properties of a deep eutectic solvent comprised of a 1:2 molar ratio of choline chloride and ethylene glycol. This mixture, known as Ethaline, has many desirable properties for use in a range of applications, and therefore, understanding its liquid structure and transport properties is of interest. Simulation results are able to capture experimental densities, diffusivities, viscosities, and structure factors extremely well. Here, the solvation environment is dynamic and dominated by different hydrogen bonding interactions. Dynamic heterogeneities resulting from hydrogen bonding interactions are quantified. Rotational dynamics of molecular dipole moments of choline and ethylene glycol are computed and found to exhibit a fast and slow mode.

25 ENERGY STORAGE↗

Data-driven optimized control of the COVID-19 epidemics

Optimizing the impact on the economy of control strategies aiming at containing the spread of COVID-19 is a critical challenge. We use daily new case counts of COVID-19 patients reported by local health administrations from different Metropolitan Statistical Areas (MSAs) within the US to parametrize a model that well describes the propagation of the disease in each area. We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time. We find that with the exception of the initial time and of the final time, the optimal control input is well approximated by a constant, specific to each area, which contrasts with the implemented system of reopening ‘in phases’. For all the areas considered, this optimal level corresponds to stricter social distancing than the level estimated from data. Proper selection of the time period for application of the control action optimally is important: depending on the particular MSA this period should be either short or long or intermediate. We also consider the case that the transmissibility increases in time (due e.g. to increasingly colder weather), for which we find that the optimal control solution yields progressively stricter measures of social distancing. We finally compute the optimal control solution for a model modified to incorporate the effects of vaccinations on the population and we see that depending on a number of factors, social distancing measures could be optimally reduced during the period over which vaccines are administered to the population.

60 APPLIED LIFE SCIENCES↗

Coulombically-stabilized oxygen hole polarons enable fully reversible oxygen redox

Stabilizing high-valent redox couples and exotic electronic states necessitates an understanding of the stabilization mechanism. In oxides, whether they are being considered for energy storage or computing, highly oxidized oxide-anion species rehybridize to form short covalent bonds and are related to significant local structural distortions. In intercalation oxide electrodes for batteries, while such reorganization partially stabilizes oxygen redox, it also gives rise to substantial hysteresis. In this work, we investigate oxygen redox in layered Na 2-x Mn 3 O 7 , a positive electrode material with ordered Mn vacancies. We prove that coulombic interactions between oxidized oxide-anions and the interlayer Na vacancies can disfavor rehybridization and stabilize hole polarons on oxygen (O-) at 4.2 V vs. Na/Na + . These coulombic interactions provide thermodynamic energy saving as large as O–O covalent bonding and enable ~40 mV voltage hysteresis over multiple electrochemical cycles with negligible voltage fade. As such, our results establish a complete picture of redox energetics by highlighting the role of coulombic interactions across several atomic distances and suggest avenues to stabilize highly oxidized oxygen for applications in energy storage and beyond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

First-principles molten salt phase diagrams through thermodynamic integration

Precise prediction of phase diagrams in molecular dynamics simulations is challenging due to the simultaneous need for long time and large length scales and accurate interatomic potentials. Here, we show that thermodynamic integration from low-cost force fields to neural network potentials trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid–liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange–correlation functionals for predicting the NaCl phase boundary and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid–liquid phase boundaries for any material at an ab initio level of accuracy, with the majority of the computational cost at the level of classical potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effects of charged interfaces on electrolyte decomposition at the lithium metal anode

Lithium–Sulfur batteries are promising candidates to substitute conventional Li-ion batteries due to their higher energy density and reduced cost. However, several challenges related to the reactivity of the lithium metal anode have prevented this technology from becoming broadly commercialized. Lithium's high reactive nature leads to the continuous decomposition of the electrolyte and the formation of the solid-electrolyte interphase (SEI) layer. Thus, a comprehensive understanding of how the SEI film is formed is crucial to help elucidate improvements for this battery technology. In this work, we use density functional theory (DFT) based computational methods to investigate the effect of charged interfaces on the electrolyte degradation at the lithium anode. Here, several electrolyte mixtures were considered including 1,2-dimethoxyethane (DME) and 1,3-dioxolane (DOL) as solvents, and lithium bis(fluorosulfonyl)imide (LiFSI) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) as salts. It is found that the extent of salt decomposition is higher when the interface is charged. In addition, two types of solvent reduction mechanisms are identified: C–O bond cleavage and radical attacks. Charge transfer and evolution analysis are characterized in detail. Finally, solvent decomposition reactions are found to become more thermodynamically favorable and their activation barriers to be diminished under the effect of constant potentials and electric fields.

25 ENERGY STORAGE↗

Computationally Guided Discovery of Axis-Dependent Conduction Polarity in NaSnAs Crystals

Most electronic materials exhibit a single dominant charge carrier type, either holes or electrons, along all crystallographic directions. However, there are a small number of compounds, mostly metals, that exhibit simultaneous p-type and n-type conduction behavior along different crystallographic directions. We demonstrate that the experimental discovery of semiconductors with this axis-dependent conduction polarity can be facilitated by identifying a large anisotropy of either the electron or hole effective masses (m*) or both, providing the electron and hole masses dominate along different crystallographic directions. We calculated the layered semiconductor NaSnAs to have a lower electron m* in-plane than the cross-plane and a very large hole m* in-plane and small hole m* cross-plane. We established the growth of >3 mm-sized NaSnAs crystals via Sn flux and confirmed the band gap to be 0.65 eV, in agreement with theory. NaSnAs exhibits p-type thermopowers cross-plane and n-type thermopowers in-plane, confirming that the large anisotropy in the effective mass at the band edges is an excellent indicator for axis-dependent conduction polarity. Altogether, this work shows that the discovery of semiconductors with such a phenomenon can be accelerated by computationally evaluating the anisotropic curvatures of the band edges, paving the way for their future discovery and application.

30 DIRECT ENERGY CONVERSION↗

A deep learning approach to programmable RNA switches

Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R 2 = 0.43–0.70) previous state-of-the-art thermodynamic and kinetic models (R 2 = 0.04–0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗

Experimental and computational kinetics study of the liquid-phase hydrogenation of C=C and C=O bonds

Solvent effects on adsorption equilibrium and reaction kinetics are evaluated for hydrogenation reactions catalyzed by Pd/alumina in a series of different solvents. Three reactants – cyclohexene, benzene and benzaldehyde – and three solvents – n-heptane (HEP), methylcyclohexane (MCHA), decalin (DL) – have been investigated. Kinetic analysis of hydrogenation of cyclohexene reveals that hydrogen adsorbs on different sites from those where cyclohexene and the solvents adsorb; however, the presence of hydrogen on these separate sites affects the heats of adsorption of the hydrocarbons. When the solvent is a weakly interacting linear alkane (HEP), the rate determining step of the reaction is the first hydrogenation of adsorbed cyclohexene. Furthermore, this conclusion is supported by DFT calculations that show a higher enthalpy barrier for the first hydrogenation than for the second, while statistical thermodynamics analysis validates the physical significance of the entropy of adsorption parameters derived from the kinetic fitting of experimental data. By contrast, with solvents such as MCHA and DL, which interact more strongly with the metal surface and compete for active sites with the reactant and the surface intermediate, the rate limiting step seems to shift to the second hydrogenation step.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High throughput exploration of the oxidation landscape in high entropy alloys

We report high entropy alloys (HEAs) have gained interest for structural applications in extreme environments. With a potentially vast chemical and phase space, there are significant opportunities to discover superior performing alloys. Crucial for most high-temperature applications is understanding and mitigating the oxidation behavior of these chemically complex alloys. Most experimental and computational HEA studies have focused on a limited set of compositions and only a fraction these compositions have been characterized for oxidation. We present a high-throughput framework that utilizes density-functional theory (DFT) in concert with a combined machine-learning model and grand-canonical linear programming for assessing phase stability, phase-fraction, chemical activity and high-temperature survivability of arbitrary HEAs. This framework considers temperature dependent contributions to the Gibbs energy of the competing phases arising from short-range order and vibrational entropy. We demonstrate the effectiveness of the framework by assessing the thermodynamic stability, oxidation behavior, chemical activity, and phase decomposition of body-centered cubic Mo-W-Ta-Ti-Zr refractory HEAs. A total of 51 compositions were analyzed and ranked in order of their survivability based on the Pareto-front analysis. Oxidation was performed at 1373 K on fours samples in air showing the difference in oxidation behavior determined experimentally through scale thickness and their mass changes. The insights on oxidation behavior presented in this work will enable the fast assessment of technologically useful HEAs needed for future structural application in extreme conditions

36 MATERIALS SCIENCE↗

Thermodynamically leveraged solventless aerobic deconstruction of polyethylene-terephthalate plastics over a single-site molybdenum-dioxo catalyst

Here, we describe the solventless catalytic deconstruction of polyethylene-terephthalate (PET) under an aerobic atmosphere, mediated by an earth-abundant, low-cost activated carbon (AC)-supported single-site molybdenum-dioxo catalyst (AC/MoO 2 ). Catalytic amounts of AC/MoO 2 selectively convert waste PET into its monomer, terephthalic acid (TPA), within 4 h at 265 °C with yields as high as 94% under 1 atm air. Pure crystalline TPA product sublimes from the reaction hot zone, crystallizing on the reactor cold zone, thus avoiding the need for separation and purification steps. This process does not employ any hazardous/toxic reducing agents or solvents, and the catalyst can be recycled multiple times without loss of activity, rendering this process highly atom-efficient. According to computational and experimental mechanistic studies, the AC/MoO 2 catalyst mediates a thermoneutral metal-catalyzed β-scission step, followed by an exothermic step that converts the vinyl benzoate intermediate to TPA and acetaldehyde using trace amounts of moisture in the air. The formation of gaseous acetaldehyde makes the isolation of TPA from the reaction mixture facile and industrially favorable, especially since solvents are unnecessary. The present methodology is also extended to the deconstruction of other frequently used polyester plastics, polybutylene terephthalate (PBT), polyethylene naphthalate (PEN), and polyethylene furanoate (PEF), and operates equally well with post-consumer waste products. Notably, this process is also compatible with plastic mixtures of polyesters with polyolefins, polyamides, and polycarbonates, leading to the selective conversion of each polyester to the corresponding monomer, leaving the residual polymer unchanged and polyester-free.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗