First-principles Energy Research Articles

Observation of critical scaling in spin glasses below Tc using thermoremanent magnetization

Time-dependent thermoremanent magnetization (TRM) studies have been instrumental in probing energy dynamics within the spin glass phase. In this paper, we review the evolution of the TRM experiment over the last half century and discuss some aspects related to how it has been used in the understanding of spin glasses. We also report on recent experiments using high-resolution DC SQUID magnetometry to probe the TRM at temperatures less than but near to the transition temperature Tc. These experiments have been performed as a function of waiting time, temperature, and five different magnetic fields. We find that as the transition temperature is approached from below, the characteristic time scale of TRM is suppressed up to several orders of magnitude in time. In the highest-temperature region, we find that the waiting time effect subsides, and a waiting time-independent crossover line is reached. We also find that increasing the magnetic field further suppresses the crossover line. Using a first-principles energy argument across the crossover line, we derive an equation that is an excellent fit to the crossover lines for all magnetic fields probed. The data show strong evidence for critical slowing down and an H = 0 Oe phase transition.

The role of point defect reconstructions and polycyclic aromatic hydrocarbons in silicate dust preservation

ABSTRACT Forsterite is a primary constituent of interstellar dust and planetary systems. It is believed to originate from the outflows of oxygen-rich stars and undergo further processing within the interstellar medium through the action of cosmic rays and shocks. Under these harsh conditions, point defects may form, such as MgO Schottky vacancies. These vacancies can then undergo atom reconstruction as part of a chemical process to maintain the system’s crystalline structure. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitously observed interstellar molecules and are thought to form through gas-phase reactions akin to sooting flames. However, their role and impact on dust stability remain unknown. In this study, we employ an atomistic artificial-intelligence-based method, surrogate machine learning trained directly by density functional theory. Specifically, we utilize gofee (global optimization with first-principles energy expressions) to predict possible reconstructions of MgO vacancies on a crystalline forsterite (010) surface as an important component of interstellar dust and planetary systems. We identify nine possible reconstructions involving the formation of unbound Si and O atoms. We investigate their energy stability and find that the reconstruction of Si–O atoms stabilizes the vacancy by about 0.54 eV. Additionally, if PAHs bind with the unbound O and Si atoms of the vacancy, the vacancy is stabilized by approximately 1.76 eV. We demonstrate that PAHs, along with the reconstruction of unbound atoms on the dust surface, affect the stability of the dust, which might open up avenues for diverse chemistry.

Accelerating structure search using atomistic graph-based classifiers.

We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a useful tool for sorting through large datasets of atomic systems. We incorporate the classifier as a filtering method in the Global Optimization with First-principles Energy Expressions (GOFEE) algorithm. Here, it is used to filter out structures from exploited regions of the potential energy landscape, whereby the risk of stagnation during the searches is lowered. We demonstrate the usefulness of the classifier by solving the global optimization problem of two-dimensional pyroxene, three-dimensional olivine, Au12, and Lennard-Jones LJ55 and LJ75 nanoparticles.

Thermodynamic modeling of Bi2Te3 in the defect energy formalism

Bi2Te3 is a promising thermoelectric material that is often touted as one of the best-performing low-temperature thermoelectric materials. As a result, it has been widely used commercially, both for clean energy generation and in cooling devices. Like many other thermoelectric materials, defects play a key role in the performance of Bi2Te3. As a result, numerous studies have attempted to experimentally and computationally map out the dominant defects in the phase, these include efforts to determine the dominant defect, estimate defect energies, and predict their concentration. The computer coupling of phase diagrams and thermochemistry (CALPHAD) is one of many tools under the auspices of the materials genome initiative (MGI) that enables the rapid design of new functional materials with improved properties. The Defect energy formalism (DEF) with a charged sublattice, an offshoot of the Compound energy formalism (CEF), provides a way to directly include first-principle defect energy calculations into CALPHAD descriptions of solid phases. The introduction of the charge sublattice enables the estimation of the free carrier concentrations in the phase. Here we apply the DEF to the Bi2Te3 system, emphasizing the robustness of the DEF in describing meaningful endmembers and the elimination of fitting parameters. Unlike previous assessments using the Wagner–Schottky defect model, we include the description of the charged defects in our assessment. The DEF with a charged sublattice provides a good prediction of the non-stoichiometry of the phase when compared with experimental data and also predicts a thermodynamic defect concentration at low temperature that is physically reasonable.

Development of a Nonlinear Model Predictive Control-Based Nonlinear Three-Mode Controller for a Nonlinear System.

This paper presents the novelty on a nonlinear proportional integral derivative (NPID) controller developed from the gain values obtained using the Lyapunov-based nonlinear model predictive controller (LyNMPC). The tuning parameters of the proposed controller are taken from the dynamics of the nonlinear system, and these parmeters are dynamic with their value varying according to the error in the system. In this article, the authors have considered two highly nonlinear systems, namely, batch polymerization reactor and quadrotor unmanned aerial vehicle systems. The nonlinear mathematical modeling of the batch reactor as well as the quadrotor system considered from the past literature of authors. The acrylamide polymerization reaction under consideration is an exothermic reaction, thereby making the temperature profile tracking and control a challenging task. The primary aim of this article is to develop the NPID controller based on the LyNMPC algorithm and to validate the NPID on a batch reactor bench-scale plant and on an hardware-in-the-loop platform for the quadrotor hardware. A comparative study of trajectory tracking and control capabilities of LyNMPC on derived non-linear models of the batch reactor and quadrotor system is presented. The system mathematical models are obtained with the help of the first-principle energy balance equation for the batch reactor and with the nonlinear dynamics of the quadrotor which is derived based on Newton-Euler formulations. With LyNMPC, the stability of the nonlinear systems can be improved because the error sensitivity is considered in the cost function.

Energy Use in Quantum Data Centers: Scaling the Impact of Computer Architecture, Qubit Performance, Size, and Thermal Parameters

As quantum computers increase in size, the total energy used by a quantum data center, including the cooling, will become a greater concern. The cooling requirements of quantum computers, which operate at temperatures near absolute zero, are determined by computing system parameters, including the number and type of physical qubits, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. When combined with thermal system parameters such as cooling efficiency and cryostat heat transfer, the total energy use can be determined using a first-principles energy model. These models show that cooling of quantum computers differs in two fundamental ways from conventional data centers: (1) the energy required for cooling is much greater than the energy required for computation, and (2) the cooling loads are sensitive to the computational architecture. The temperature requirements for different qubit types can change energy requirements by orders of magnitude. Power use and computational power, as quantified by quantum volume, are analytically correlated. Approaches are identified for minimizing energy use in integrated quantum systems relative to computational power. Designing a sustainable quantum computer will require both efficient cooling and system design that minimizes cooling requirements.

Substitutional Tungsten Doping in Silicon Carbide Introducing Magnetic Properties: A Computational DFT Approach

Background: Small concentration of magnetic material, in general, the transition metal atoms (TM), when doped into a semiconductor, behaves as a diluted magnetic semiconductor (DMS). It has an application to Quantum computing & spintronic devices. DMS silicon carbide has strong coupling and high Curie temperature. The magnetic and electronic properties of SiC with TMs impurities have been in focus for theoretical and experimental researchers. Objective: The objective of this work is to study the electrical and magnetic properties of tungsten doped cubic SiC. Comparing the density of states plot with and without impurity, the change in property happening due to the presence of tungsten is observed. Partial density of states is also plotted and interpreted. Self-consistent spin polarized calculations are done to study the magnetic properties. Magnetic Moment is also calculated for substitutional doping of SiC at different sites. Methods: Tungsten doped 3C-SiC is investigated by using the first-principle energy code, Quantum Espresso, which uses pseudopotential within Density Functional Theory (DFT). The calculations are done by density functional pseudopotential energy calculations in periodic systems by solving iteratively the Kohn Sham equation in a plane wave basis set. Both norm conserving and Vanderbilt USPP are used. Self-consistent iterations were performed until convergence of total energy and total charge was obtained. We used different k-point meshes for different supercells with 16, 54 and 128 atoms giving results for carious impurity percentages. Results: The formation energy values obtained indicate that the W impurity prefers the Si site to the C site in cubic SiC. The presence of a narrow band towards the conduction band minimum is due to the W-d states for Si site substitution. Both spin-up and spin-down states contribute towards the valence band, and a small contribution goes towards the conduction band. The magnetic moment values for C site substitution are lower than Si site substitution. Conclusion: It is observed W doped with Si site of cubic silicon carbide shows ferromagnetic behavior. Hence, there is a possibility of 3C SiC doped with W at the C site to behave as a semi-insulating material.

Global optimization of atomic structure enhanced by machine learning

Global optimization with first-principles energy expressions (GOFEE) is an efficient method for identifying low-energy structures in computationally expensive energy landscapes such as the ones described by density functional theory (DFT), van der Waals enabled DFT, or even methods beyond DFT. GOFEE is an evolutionary algorithm, that in order to explore configuration space creates several candidates in parallel. These are treated approximately using a machine learned surrogate model of energies and forces, trained on the fly, eliminating the need for expensive relaxations using first-principles methods. Eventually, using Bayesian statistics, GOFEE chooses one candidate and treats that at the full first-principles level. In this paper we elaborate on the importance of the use of a Gaussian kernel with two length scales in the Gaussian process regression surrogate model. We further explore the role of the use in GOFEE of the lower confidence bound for relaxation and selection of candidate structures. In addition, we present details of a sampling scheme for obtaining parent structures in the evolution. Using machine learning clustering of the entire pool of low-energy structures ever calculated, and choosing the most stable member from each cluster, the scheme ensures a highly diverse sample of structures that plays the role of a population. The versatility of the GOFEE method is demonstrated by applying it to identify the low-energy structures of gas-phase fullerene-type 24-atom carbon clusters and of dome-shaped 18-atom carbon clusters supported on Ir(111).

Nonlinear deformation and elasticity of BCC refractory metals and alloys

Application of isotropic pressure or uniaxial strain alters the elastic properties of materials; sufficiently large strains can drive structural transformations. Linear elasticity describes stability against infinitesimal strains, while nonlinear elasticity describes the response to finite deformations. It was previously shown that uniaxial strain along [100] drives refractory metals and alloys towards mechanical instabilities. These include an extensional instability, and a symmetry-breaking orthorhombic distortion caused by a Jahn-Teller-Peierls instability that splays the cubic lattice vectors. Here we analyze these transitions in depth. Eigenvalues and eigenvectors of the Wallace tensor identify and classify linear instabilities in the presence of strain. We show that both instabilities are discontinuous, leading to discrete jumps in the lattice parameters. We provide physical intuition for the instabilities by analyzing the changes in first-principles energy, stress, bond lengths, and angles upon application of strain. Electronic band structure calculations show differential occupation of bonding and antibonding orbitals, driven by the changing bond lengths and leading to the structural transformations. Strain thresholds for these instabilities depend on the valence electron count.

Unravelling Morphological and Topological Energy Contributions of Metal Nanoparticles.

Metal nanoparticles (NPs) are ubiquitous in many fields, from nanotechnology to heterogeneous catalysis, with properties differing from those of single-crystal surfaces and bulks. A key aspect is the size-dependent evolution of NP properties toward the bulk limit, including the adoption of different NP shapes, which may bias the NP stability based on the NP size. Herein, the stability of different Pdn NPs (n = 10–1504 atoms) considering a myriad of shapes is investigated by first-principles energy optimisation, leading to the determination that icosahedron shapes are the most stable up to a size of ca. 4 nm. In NPs larger than that size, truncated octahedron shapes become more stable, yet a presence of larger {001} facets than the Wulff construction is forecasted due to their increased stability, compared with (001) single-crystal surfaces, and the lower stability of {111} facets, compared with (111) single-crystal surfaces. The NP cohesive energy breakdown in terms of coordination numbers is found to be an excellent quantitative tool of the stability assessment, with mean absolute errors of solely 0.01 eV·atom−1, while a geometry breakdown allows only for a qualitative stability screening.

First-principles energy band calculation of Pr-doped ZrSiO<sub>4</sub>

First-principles energy band calculation of Pr-doped ZrSiO<sub>4</sub>

InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network.

Peptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modeling such interactions is to exhaustively sample the conformational space by fast-Fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection fast enough for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical potentials. We present InterPepRank for peptide-protein complex scoring and ranking. InterPepRank is a machine learning-based method which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary and sequence features as nodes. The graph network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys. InterPepRank is tested on a massive independent test set with no targets sharing CATH annotation nor 30% sequence identity with any target in training or validation data. On this set, InterPepRank has a median AUC of 0.86 for finding coarse peptide-protein complexes with LRMSD < 4Å. This is an improvement compared to other state-of-the-art ranking methods that have a median AUC between 0.65 and 0.79. When included as a selection-method for selecting decoys for refinement in a previously established peptide docking pipeline, InterPepRank improves the number of medium and high quality models produced by 80% and 40%, respectively. The InterPepRank program as well as all scripts for reproducing and retraining it are available from: http://wallnerlab.org/InterPepRank .

Synergetic Layer-By-Layer Deposition for Highly Capacitive CNT Electrodes

While inorganic materials are currently dominating in energy storage, organic redox active molecules and polymers are sustainable and low-cost alternatives. Thin layer redox active polymers and molecules are often deposited onto high surface area carbonaceous substrates to form high-performance composite electrodes. Layer-by-layer (LbL) deposition has been widely used in such deposition to provide versatile electrochemical properties for specific applications [1, 2]. The advantages of LbL are its easy and effective processes and, more importantly, allowing design and engineering of novel composites. Through simple superimposing and overlapping of organic molecules, combined supplementary effects and even synergies can be achieved.In this study, a composite electrode based on CNT modified with two previously investigated organic layers, a chemically polymerized luminol (CpLum) [3] and a tetraphenylporphyrin sulfonate (TPPS), was developed via an in-situ polymerization/layer-by-layer process. Fig 1 a) depicts the CVs of CNT, CpLum-CNT, TPPS-CNT as well as TPPS-CpLum-CNT electrodes in 1 M H2SO4 electrolyte. TPPS-CNT showed clear increase in capacitive current from CNT baseline due to the redox contribution from TPPS. CpLum-CNT had a much greater faradaic current but with much less “mirror-image” CV profile, indicating a slower kinetics. When combining CpLum and TPPS by LbL deposition, a new pseudocapacitive CV profile with highly reversible redox peaks was obtained. The TPPS-CpLum-CNT composite electrode not only had higher current and capacitance (Fig. 1b) than those of the individual composite electrodes but also a much faster charge transfer kinetics. The synergy within the composite promoted the complementary electrochemical contribution between the CpLum and the TPPS layer with fast surface capacitive charge storage. Detailed analyses of this composite from first-principles adsorption and energy level, vibrational spectroscopy as well as surface and morphology were carried out to understand the origin of the synergistic effect of the TPPS-CpLum-CNT composite and will be presented in the talk.

Importance of Nuclear Quantum Effects for NMR Crystallography.

The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential energy. To test the limits of this approximation, we systematically study the importance of finite-temperature and quantum nuclear fluctuations for 1H, 13C, and 15N shieldings in polymorphs of three paradigmatic molecular crystals: benzene, glycine, and succinic acid. The effect of quantum fluctuations is comparable to the typical errors of shielding predictions for static nuclei with respect to experiments, and their inclusion improves the agreement with measurements, translating to more reliable assignment of the NMR spectra to the correct candidate structure. The use of integrated machine-learning models, trained on first-principles energies and shieldings, renders rigorous sampling of nuclear fluctuations affordable, setting a new standard for the calculations underlying NMR structure determinations.

Understanding phase equilibria in high-entropy alloys: II. Atomic-scale study of incorporation of metallic elements in Cr carbides – Application to equilibrium with AlCrFeMnMo

Using atomic-scale first-principles energy calculations, we describe a methodology allowing to investigate the effect of metallic elements in frequently encountered Cr23C6 carbides, and its application to the equilibrium between Cr23C6 and AlCrFeMnMo high-entropy solid solutions. Our study reveals the importance of taking properly into account the effect of interstitial C in Cr23C6, since the latter is found to have a key-influence on the thermodynamics of the compound. Moreover, it emphasizes clear-cut trends as regards the propensity of the various metallic elements to penetrate the carbide. Finally, it illustrates the role of the chemical potentials in the high-entropy solid solution, since the latter are the key-quantities controlling the composition of Cr23C6. The tractability of the approach described here should allow easy applications to similar cases, including other chemically complex solid solutions, as well as various second-phase particles (carbides, nitrides, oxides, borides…) unavoidably formed during elaboration processes.

The effect of defect interactions on the reduction of doped ceria.

Doped cerium oxide is known for its reduction properties that are utilized in catalytic applications as well as in thermochemical cycling to produce solar fuels. Upon reduction of the lattice, oxygen vacancies and polarons are formed leading to a highly concentrated solution of defects in which the interactions of defects cannot be neglected anymore. In this study, the effect of defect interactions on the free energy of reduction for doped ceria with composition Ce1-x-yRExZryO2-x/2-δ was investigated by large scale Metropolis Monte Carlo multi-stage sampling simulations based on first-principles calculations. The simulations allowed the prediction of the relation between oxygen partial pressure and non-stoichiometry for the highly interacting, non-ideal system. The results show that the non-ideality observed in experiments can be traced back to the interactions of defects and allow prediction of the reduction behavior for various dopant types, dopant fractions, temperatures, and non-stoichiometries.

Electronic structure of LiOsO3: Electron energy-loss spectroscopy and first-principles study

Electronic structure of LiOsO3 were investigated by using first-principle calculations and electron energy loss spectroscopy (EELS). According to the calculated densities of states, the interband transitions from valence band (VB) to conduction band (CB) to the low energy loss spectrum were assigned. The hybridization of O 2p with Os 5d was critical to the coexistence of metal and ferroelectricity in LiOsO3. From the simulation of the energy loss near-edge structure (ELNES), the fine structure of O K-edge was in good agreement with the experimental spectrum, while the core hole effect and spin-orbital coupling had little effect on the spectra.

Quantifying the uncertain effects of climate change on building energy consumption across the United States

Climate change could have both positive and negative effects on the energy consumption of buildings. Today, it is not clear what the extent of these effects could be at multiple spatial scales including building sectors, cities, and climate zones. More importantly, the uncertainty of mathematical models used to estimate these effects is not well understood. This knowledge gap makes it difficult to evaluate decisions about what buildings, cities, and even technologies should be prioritized in the race to mitigate climate change. Moreover, this lack of knowledge makes it difficult for researchers to build on the limitations of past models effectively. To address this knowledge gap, we develop a novel framework for quantifying model uncertainty in the context of climate change and building energy consumption. The framework blends for the first time large sources of weather and building energy consumption data with Bayesian statistics and first-principle building energy models. The framework is used to forecast the potential effects of climate change in buildings across 96 cities in the United States for the 21st century. Based on our estimates and credible intervals, we found reasons to support the idea that commercial buildings in hot/warm and humid climates should be at the top of the agenda of climate action in the building sector of the United States. We believe that future research on uncertainty quantification should take a closer look at the local effects of extreme events rather than yearly effects of climate change on buildings.

Insights into the structures, energies and electronic properties of nesquehonite surfaces by first-principles calculations

By using the first-principles calculations, the structure, energies and electronic properties of four commonly exposed surfaces for the nesquehonite crystal were investigated. The needle-like nesquehonite whisker is well developed with smooth side faces and irregular hexagonal end faces. Surface energy results indicate that the (101) surface is the most stable surface and corresponds to the side face. The density of dangling bond has a positive relationship with surface energy and the (101) surface has the least dangling bonds. In terms of relaxed surface energy, the order of relaxed surfaces is (101) < (200)-H < (301) < (200)-M < (004). During surface relaxation, the changes in the length of Mg-O bonds and hydrogen bonds contribute to generating a more stable surface with a lower surface energy. The PDOS (partial density of states) of these surfaces are mainly dominated by Mg and O atoms. A small peak value is found in the PDOS of (101) and (301) surfaces, which have less exposed Mg-O bonds. Electron transfer causes changes in the length of Mg-O bonds. A more active surface will obtain a larger value of transferred electrons during surface relaxation.

Quantifying the Uncertain Effects of Climate Change on Building Energy Consumption Across the United States

Climate change could have both positive and negative effects on the energy consumption of buildings. Today, it is not clear what the extent of these effects could be at multiple spatial scales including building sectors, cities, and climate zones. More importantly, the uncertainty of mathematical models used to estimate these effects is not well understood. This knowledge gap makes it difficult to evaluate decisions about what buildings, cities, and even technologies should be prioritized in the race to mitigate climate change. Moreover, this lack of knowledge makes it difficult for researchers to build on the limitations of past models effectively. To address this knowledge gap, we develop a novel framework for quantifying model uncertainty in the context of climate change and building energy consumption. The framework blends for the first time large sources of weather and building energy consumption data with Bayesian statistics and first-principle building energy models. The framework is used to forecast the potential effects of climate change in buildings across 96 cities in the United States for the 21st century. Based on our estimates and credible intervals, we found reasons to support the idea that commercial buildings in warm and humid climates should be at the top of the agenda of climate action in the building sector of the United States. We believe that future research on uncertainty quantification should take a closer look at the local effects of extreme events rather than yearly effects of climate change on buildings.