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The increasing occurrence of extreme weather events is challenging power grid operation. For extreme weather events, the system operator is responsible for estimating the power outages and scheduling the restoration resources. This paper proposes an outage evaluation framework to identify the possible unserved load profiles, vulnerable areas, and mobile energy adequacy. The outputs of an outage prediction model tool are used to generate numerous faulted line scenarios. Next, each scenario's nodal unserved load profile is obtained by solving a three-phase restoration model that considers repair crews and mobile energy resources (MERs). Then, a novel scenario clustering strategy is developed to cluster the unserved load profiles into multiple representative profiles which the system operator can focus on. Finally, case studies on a distribution system evaluate the damage caused by an extreme weather event and verify the effectiveness of the proposed scenario clustering strategy.
We explore band structure effects to help determine the suitability of n-type type-II superlattice (T2SL) absorbers for infrared detectors. It is often assumed that the exceedingly large growth-direction band-edge curvature hole effective mass in n-type long wavelength infrared (LWIR) T2SL would lead to low hole mobility and therefore low detector collection quantum efficiency. We computed the thermally averaged conductivity effective mass and show that the LWIR T2SL hole conductivity effective mass along the growth direction can be orders of magnitude smaller than the corresponding band-edge effective mass. LWIR InAs/GaSb T2SL can have significantly smaller growth-direction hole conductivity effective mass than its InAs/InAsSb counterpart. For the InAs/InAsSb T2SL, higher Sb fraction is more favorable for hole transport. Achieving long hole diffusion length becomes progressively more difficult for the InAs/InAsSb T2SL as the cutoff wavelength increases, since its growth-direction hole conductivity effective mass increases significantly with decreasing band gap. However, this is mitigated by the fact that the splitting between the top valence subbands also increases with the cutoff wavelength, leading to reduced inter-subband scattering and increased relaxation time.
We report Perturbo is a software package for first-principles calculations of charge transport and ultrafast carrier dynamics in materials. The current version focuses on electron–phonon interactions and can compute phonon-limited transport properties such as the conductivity, carrier mobility and Seebeck coefficient. It can also simulate the ultrafast nonequilibrium electron dynamics in the presence of electron–phonon scattering. Perturbo uses results from density functional theory and density functional perturbation theory calculations as input, and employs Wannier interpolation to reduce the computational cost. It supports norm-conserving and ultrasoft pseudopotentials, spin–orbit coupling, and polar electron–phonon interactions for bulk and 2D materials. Hybrid MPI plus OpenMP parallelization is implemented to enable efficient calculations on large systems (up to at least 50 atoms) using high-performance computing. Taken together, Perturbo provides efficient and broadly applicable ab initio tools to investigate electron–phonon interactions and carrier dynamics quantitatively in metals, semiconductors, insulators, and 2D materials.
In this work, we describe details of a two-step deposition approach that enables the preparation of continuous and well-structured thin films of Cs 2 SnI 6 , which is a one-half Sn-deficient 0-D perovskite derivative (i.e., the compound can also be written as CsSn 0.5 I 3 , with a structure consisting of isolated SnI 6 4- octahedra). The films were characterized using powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), UV-Vis spectroscopy, photoluminescence (PL), photoelectron spectroscopy (UPS, IPES, XPS) and Hall effect measurements. UV-Vis and PL measurements indicate that the obtained Cs 2 SnI 6 film is a semiconductor with a band gap of 1.6 eV. This band gap was further confirmed by the UPS and IPES spectra, which were well reproduced by the calculated density of states with the HSE hybrid functional. The Cs 2 SnI 6 films exhibited n-type conduction with a carrier density of 6(1) × 10 16 cm -3 and mobility of 2.9(3) cm 2 /V·s. While the computationally-derived band structure for Cs 2 SnI 6 shows significant dispersion along several directions in the Brillouin zone near the band edges, the valence band is relatively flat along the $Γ$–X direction, indicative of a more limited hole minority carrier mobility compared to analogous values for the electrons. The ionization potential (IP) and electron affinity (EA) were determined to be 6.4 and 4.8 eV, respectively. The Cs 2 SnI 6 films show some enhanced stability under ambient air, compared to methylammonium lead (II) iodide perovskite films stored under similar conditions; however, the films do decompose slowly, yielding a CsI impurity. We discuss these findings in the context of suitability of Cs 2 SnI 6 for photovoltaic and related optoelectronic applications.
Ion-conducting polymer electrolytes are attractive materials for all-solid-state lithium-ion batteries (LiBs) as a result of their potential to improve performance, mitigate safety concerns, and facilitate efficient device processing. Understanding how lithium-ionic transport is influenced by the macromolecular features, such as dielectric strength, solvation site connectivity, monomer segment relaxation timescales, segregation strength, and mesophase composition, is crucial for advancing ion-conducting materials. The integration of experimental and computational efforts can provide comprehensive links between chemical and structural characteristics (e.g., ion solvation, chain mobility, morphology) and macroscopic performance properties, accelerating the adoption of next-generation polymer electrolytes. Furthermore, this perspective highlights several synergistic experimental–computational approaches that have provided unique insights into local ion and polymer interactions, chain dynamics, and thermodynamics to promote the rational design of new ion-conducting polymers. Additionally, this work concludes with several suggested areas in which methodologies can be augmented, novel properties can be realized, and polymer electrolytes can be integrated to generate more robust LiB systems.
Sampling equilibrium configurations of correlated systems of particles with long relaxation times (e.g., polymeric solutions) using conventional molecular dynamics and Monte Carlo methods can be challenging. This is especially true for systems with complicated, extended bond network topologies and other interactions that make the use and design of specialized relaxation protocols infeasible. We introduce a method based on Brownian dynamics simulations that can reduce the computational time it takes to reach equilibrium and draw decorrelated samples. Importantly, the method is completely agnostic to the particle configuration and the specifics of interparticle forces. In particular, we develop a mobility matrix that excites non-local, collective motion of N particles and can be computed efficiently in $\mathcal{O}(N)$ time. Particle motion in this scheme is computed by integrating the overdamped Langevin equation with an Euler–Maruyama scheme, in which Brownian displacements are drawn efficiently using a low-rank representation of the mobility matrix in position and wave space. We demonstrate the efficacy of the method with various examples from the realm of soft condensed matter and release a massively parallel implementation of the code as a plugin for the open-source package HOOMD-blue [J. A. Anderson et al., J. Comput. Phys. 227, 5342 (2008) and J. Glaser et al., Comput. Phys. Commun. 192, 97 (2015)] which runs on graphics processing units
The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Successful Energy Project Implementation" will explore common challenges in implementing energy projects and learn strategies to turn project plans into reality. Each participant will focus on a local energy project or priority as a cornerstone for their learning throughout the series.This presentation overviews the basics of developing electric mobility projects with emphasis on leveraging the right tools to advance technical scoping and program design.
We report strengthening in complex multicomponent systems such as solid solution alloys is controlled primarily by the dynamic interactions between dislocation lines and heterogeneously distributed solute species. Modeling of extended defect length scales in such multicomponent systems becomes prohibitively expensive, motivating the development of reduced order approaches. This work explores the application of the Concurrent Atomistic-Continuum (CAC) method to model dislocation mobility in random alloys at extended length scales. By employing recently developed average-atom interatomic potentials, the average “bulk” material response in coarse-grained regions interacts with true random solute species in the atomistic-scale domain. We demonstrate that spurious stresses in domain resolution transition regions are eliminated entirely due to the CAC formulation. Simultaneously, the key details of local stress fluctuation due to randomness in the dislocation core region are captured, and fluctuating stress smoothly decays to the long-range dislocation stress field response. Dislocation mobility calculations, for line lengths over 400 nm, are computed as a function of alloy composition in the model FeNiCr system and compared to full molecular dynamics (MD). The results capture the composition-dependent trends, while reducing degrees of freedom by nearly 40%. This approach can be readily extended to any system described by an EAM potential and facilitates the study of large-scale defect dynamics in complex solute environments to support computational alloy design.
Binary neural networks (BNNs) show promising utilization in cost and power-restricted domains such as edge devices and mobile systems. This is due to its significantly less computation and storage demand, but at the cost of degraded performance. To close the accuracy gap, in this paper we propose to add a complementary activation function (AF) ahead of the sign based binarization, and rely on the genetic algorithm (GA) to automatically search for the ideal AFs. These AFs can help extract extra information from the input data in the forward pass, while allowing improved gradient approximation in the backward pass. Fifteen novel AFs are identified through our GA-based search, while most of them show improved performance (up to 2.54% on ImageNet) when testing on different datasets and network models. Interestingly, periodic functions are identified as a key component for most of the discovered AFs, which rarely exist in human designed AFs. Our method offers a novel approach for designing general and application-specific BNN architecture.
Mobility in America is changing fast. To understand where it’s headed, Argonne caught up with Michael Berube, the acting deputy assistant secretary for transportation in the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy. While at the laboratory, Berube took time to highlight the ways in which technology and consumer demand are shaping transportation, and the insights national laboratories like Argonne can provide about the future.
A method for relating traveling-wave ion mobility spectrometry (TWIMS) drift times with collisional cross-sections using computational simulations is presented. This method is developed using SIMION modeling of the TWIMS potential wave and equations that describe the velocity of ions in gases induced by electric fields. The accuracy of this method is assessed by comparing the collisional cross-sections of 70 different reference ions obtained using this method with those obtained from static drift tube ion mobility measurements. The cross-sections obtained here with low wave velocities are very similar to those obtained using static drift (average difference = 0.3%) for ions formed from both denaturing and buffered aqueous solutions. In contrast, the cross-sections obtained with high wave velocities are significantly greater, especially for ions formed from buffered aqueous solutions. These higher cross-sections at high wave velocities may result from high-order factors not accounted for in the model presented here or from the protein ions unfolding during TWIMS. Results from this study demonstrate that collisional cross-sections can be obtained from single TWIMS drift time measurements, but that low wave velocities and gentle instrument conditions should be used in order to minimize any uncertainties resulting from high-order effects not accounted for in the present model and from any protein unfolding that might occur. Thus, the method presented here eliminates the need to calibrate TWIMS drift times with collisional cross-sections measured using other ion mobility devices. .
Structural phase transitions and soft phonon modes pose a longstanding challenge to computing electron-phonon (e-ph) interactions in strongly anharmonic crystals. Here we develop a first-principles approach to compute e-ph scattering and charge transport in materials with anharmonic lattice dynamics. Our approach employs renormalized phonons to compute the temperature-dependent e-ph coupling for all phonon modes, including the soft modes associated with ferroelectricity and phase transitions. We show that the electron mobility in cubic SrTiO 3 is controlled by scattering with longitudinal optical phonons at room temperature and with ferroelectric soft phonons below 200 K. Our calculations can accurately predict the temperature dependence of the electron mobility in SrTiO 3 between 150-300 K, and reveal the microscopic origin of its roughly T -3 trend. Our approach enables first-principles calculations of e-ph interactions and charge transport in broad classes of crystals with phase transitions and strongly anharmonic phonons.
Efficient energy storage systems are crucial to address the intermittency of renewable energy sources. As multivalent batteries, Zn-ion batteries (ZIBs), while inherently low voltage, offer a promising low-cost alternative to Li-ion batteries due to the viable use of zinc as the anode. However, to maximize the potential impact of ZIBs, rechargeable cathodes with improved Zn diffusion are needed. To better understand the chemical and structural factors influencing Zn-ion mobility within battery electrode materials, we employ a high-throughput computational screening approach to systematically evaluate candidate intercalation hosts for ZIB cathodes, expanding the chemical search space on empty intercalation hosts that do not contain Zn. We leverage a high-throughput screening funnel to identify promising cathodes in ZIBs, integrating screening criteria with density functional theory (DFT)-based calculations of Zn2+ intercalation and diffusion inside the host materials. Using these data, we identify the design principles that favor Zn-ion mobility in candidate cathode materials. Building on previous work on divalent-ion cathodes, this study broadens the chemical space for next-generation multivalent energy storage systems.
The National Renewable Energy Laboratory (NREL) has deep experience developing secure, national data repositories, which it augments with analysis, technology and market expertise, high-performance computing, and innovative data visualization. By adapting the architecture of existing mobility databases (i.e., Fleet DNA, the Transportation Secure Data Center, the National Fuel Cell Technology Evaluation Center), NREL can build a powerful aviation data clearinghouse - Flight DNA - that helps stakeholders navigate the web of pitfalls and possibilities generated by new aviation technologies.
Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet rebalancing. For this reason, operators tend to employ simplified algorithms that have been demonstrated to work well in a particular setting. To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost. In particular, by treating dispatch as part of the environment dynamics, a centralized agent can learn to intermittently direct the dispatcher to reposition free vehicles and mitigate against fleet imbalance. We formulate RL state and action spaces as distributions over a grid partitioning of the operating area, making the framework scalable and avoiding the complexities associated with multiagent RL. Numerical experiments, using real-world trip and network data, demonstrate that RL reduces waiting time by 28% to 38% for the same-day evaluation, 17% to 44% for cross-day evaluation, and 22% to 25% for cross-season evaluation compared with no rebalancing scenarios. This approach has several distinct advantages over baseline methods including: improved system cost; high degree of adaptability to the selected dispatch method; and the ability to perform scale-invariant transfer learning between problem instances with similar vehicle and request distributions.
This slide deck provides an update on the progress of the project titled "MEP Core Tools" funded by the Energy Efficient Mobility Systems program in the Vehicle Technologies Office at the Department of Energy.
Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.