Accurate Energy Research Articles

Enhancing MPPT efficiency in PV systems under partial shading: A hybrid POA&PO approach for rapid and accurate energy harvesting

Partial shading in operational environments introduces multiple peaks in the output characteristics of photovoltaic (PV) systems, presenting significant challenges to energy harvesting. This study introduces a novel meta-heuristic algorithm, termed POA&PO, which aims to address the maximum power point tracking (MPPT) issues in PV systems. The algorithm capitalizes on the global search capability of the POA method to quickly pinpoint the range with the maximum power, followed by the fast convergence of the PO method to ensure both rapidity and accuracy of the solution. Extensive simulation tests, conducted in MATLAB/SIMULINK, have demonstrated the efficacy of the POA&PO algorithm, achieving an average tracking efficiency of 99.97 % with a convergence time of 0.3 s in step response tests; under the EN50530 test standard, the algorithm also showed sustained and stable tracking of ramp signals. Moreover, practical testing utilizing a new, low-cost indoor PV simulator confirmed the algorithm’s high performance under controlled conditions, yielding an average tracking efficiency of 97.03 % and a convergence time of 0.18 s. This paper highlights the capacity of the developed algorithm to reliably, accurately, and swiftly achieve high energy transfer efficiency. Additionally, the innovative and economical experimental testing methods employed are emphasized, contributing to the practical applicability and cost-effectiveness of the proposed solution.

Equivariant Neural Networks Utilizing Molecular Clusters for Accurate Molecular Crystal Lattice Energy Predictions.

Equivariant neural networks have emerged as prominent models in advancing the construction of interatomic potentials due to their remarkable data efficiency and generalization capabilities for out-of-distribution data. Here, we expand the utility of these networks to the prediction of crystal structures consisting of organic molecules. Traditional methods for computing crystal structure properties, such as plane-wave quantum chemical methods based on density functional theory (DFT), are prohibitively resource-intensive, often necessitating compromises in accuracy and the choice of exchange-correlation functional. We present an approach that leverages the efficiency, and transferability of equivariant neural networks, specifically Allegro, to predict molecular crystal structure energies at a reduced computational cost. Our neural network is trained on molecular clusters using a highly accurate Gaussian-type orbital (GTO)-based method as the target level of theory, eliminating the need for costly periodic DFT calculations, while providing access to all families of exchange-corelation functionals and post-Hartree-Fock methods. The trained model exhibits remarkable accuracy in predicting lattice energies, aligning closely with those computed by plane-wave based DFT methods, thus representing significant cost reductions. Furthermore, the Allegro network was seamlessly integrated with the USPEX framework, accelerating the discovery of low-energy crystal structures during crystal structure prediction.

Accelerated Computer-Aided Screening of Optical Materials: Investigating the Potential of Δ-SCF Methods to Predict Emission Maxima of Large Dye Molecules.

Accurate simulation of electronic excited states of large chromophores is often difficult due to the computationally expensive nature of existing methods. Common approximations such as fragmentation methods that are routinely applied to ground-state calculations of large molecules are not easily applicable to excited states due to the delocalized nature of electronic excitations in most practical chromophores. Thus, special techniques specific to excited states are needed. Δ-SCF methods are one such approximation that treats excited states in a manner analogous to that for ground-state calculations, accelerating the simulation of excited states. In this work, we employed the popular initial maximum overlap method (IMOM) to avoid the variational collapse of the electronic excited state orbitals to the ground state. We demonstrate that it is possible to obtain emission energies from the first singlet (S1) excited state of many thousands of dye molecules without any external intervention. Spin correction was found to be necessary to obtain accurate excitation and emission energies. Using thousands of dye-like chromophores and various solvents (12,318 combinations), we show that the spin-corrected initial maximum overlap method accurately predicts emission maxima with a mean absolute error of only 0.27 eV. We further improved the predictive accuracy using linear fit-based corrections from individual dye classes to achieve an impressive performance of 0.17 eV. Additionally, we demonstrate that IMOM spin density can be used to identify the dye class of chromophores, enabling improved prediction accuracy for complex dye molecules, such as dyads (chromophores containing moieties from two different dye classes). Finally, the convergence behavior of IMOM excited state SCF calculations is analyzed briefly to identify the chemical space, where IMOM is more likely to fail.

Seasonal snow–atmosphere modeling: let's do it

Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

Incidence Angle Effect: Validation of New Measurement Methods for IEC 61853‐2

ABSTRACTThe incidence angle effect causes a decrease in the photogenerated current of PV modules when they are subject to incident irradiance at wide angles: Its relevance should be quantified for accurate energy yield purposes and has recently gained significance due to the rising interest in novel integrated PV applications, where vertical or nonoptimal tilt are favored (e.g., in urban structures, in agrivoltaics, and vehicles). The international standard IEC 61853‐2 presents both outdoor and indoor measurement methods: However, the indoor measurement method for commercial‐size modules is often impractical due to irradiance uniformity limitations on the volume spanned by the tested module upon rotation in most of the solar simulators available on the market. In recent years, new solutions have been proposed to overcome these limitations and allow wider adoption of this standard: However, method validations and interlaboratory comparisons have been conducted so far only on small‐area samples, and a real validation on commercial‐size modules is still missing. In this work, we aim at filling this gap, reporting the results of an interlaboratory comparison conducted within the international project team that is currently working at the new edition of IEC 61853‐2. The results show a remarkable agreement between different measurement methods, thus validating more options for the evaluation of this important effect.

Why Does the Optimal Tuning Method of the Range Separation Parameter of a Long-Range Corrected Density Functional Fail in Intramolecular Charge Transfer Excitation Calculations?

We performed intra- and intermolecular charge transfer (CT) excitation energy calculations of (a) conjugated carbon chain [H2N-(CH=CH)n-X] and (b) its equidistant H2NH∙∙∙HX (n = 2~8) with various electron acceptors (X = NH2, OH, Cl, CHO, CN, and NO2) using EOM-CCSD, time-dependent (TD) Hartree-Fock (HF) and various density functional theory (DFT) functionals, such as BLYP, B3LYP, long-range corrected (LC) DFT, and LC-DFT with an optimally tuned (OT) range separation parameter (µ) using Koopman's theorem to investigate the effect of the electron-withdrawing (or -donating) strength of end-capped functional group (X) and CT distance (R) on intra- and intermolecular CT excitation energies. As the electron-withdrawing strength of X increases, both intra- and intermolecular CT excitation energies tend to decrease, since energy gaps between orbitals corresponding to CT excitations (e.g., HOMO and LUMO) decrease. However, the effect of the electron-withdrawing group on intramolecular CT excitation energy is negligible (at most 0.5 eV). OT-LC-DFT shows accurate intermolecular CT excitation energy, but worse results in intramolecular CT excitation energy than LC-DFT with the default µ value (0.47). Therefore, we conclude that the optimal tuning method is not effective in predicting intramolecular CT excitation energy. While intermolecular CT excitation energy has excitonic binding energy with asymptotic behavior to CT distance that is not affected by the choice of range separation parameter, intramolecular CT excitation energy is affected by orbital relaxation energy, which strongly depends on the choice of range separation parameter, which makes the OT method of range separation parameter ineffective in predicting intramolecular CT excitation energy as well as local excitation with high accuracy.

A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and machine learning (ML) schemes are employed to obtain CVs from molecular dynamics (MD)/Monte Carlo (MC) trajectories or structural databanks for biomolecules. However, minimum sampling conditions to generate reliable CVs that accurately describe the underlying energy landscape remain unclear. Here, we address this issue by developing a Mode evolution Metric (MeM) to extract CVs that can pinpoint new states and describe local transitions in the vicinity of a reference minimum from nonequilibrated MD/MC trajectories. We present a general mathematical formulation of MeM for both statistical dimensionality reduction and machine learning approaches. Application of MeM to MC trajectories of model potential energy landscapes and MD trajectories of solvated alanine dipeptide reveals that the principal components which locate new states in the vicinity of a reference minimum emerge well before the trajectories locally equilibrate between the associated states. Finally, we demonstrate a possible application of MeM in designing efficient biased sampling schemes to construct accurate energy landscape slices that link transitions between states. MeM can help speed up the search for new minima around a biomolecular conformational state and enable the accurate estimation of thermodynamics for states lying on the energy landscape and the description of associated transitions.

Particle-Particle Random Phase Approximation for Predicting Correlated Excited States of Point Defects.

The particle-particle random phase approximation (ppRPA) within the hole-hole channel was recently proposed as an efficient tool for computing excitation energies of point defects in solids [J. Phys. Chem. Lett. 2024, 15, 2757-2764]. In this work, we investigate the application of ppRPA within the particle-particle channel for predicting correlated excited states of point defects, including the carbon-vacancy (VC) in diamond, the oxygen-vacancy (VO) in magnesium oxide (MgO), and the carbon dimer defect (CBCN) in two-dimensional hexagonal boron nitride (h-BN). Starting from a density functional theory calculation of the (N - 2)-electron ground state, vertical excitation energies of the N-electron system are obtained as the differences between the two-electron addition energies. We show that active-space ppRPA with the B3LYP functional yields accurate excitation energies, with errors mostly smaller than 0.1 eV for tested systems compared to available experimental values. We further develop a natural transition orbital scheme within ppRPA, which provides insights into the multireference character of defect states. This study, together with our previous work, establishes ppRPA as a low-cost and accurate method for investigating excited-state properties of point defect systems.

Reaction Rate Rules of Intramolecular H-Migration Reaction Class for RIORIIOO·Radicals in Ether Combustion.

The intramolecular H-migration reaction of RIORIIOO· radicals constitute a key class of reactions in the low-temperature combustion mechanism of ethers. Despite this, there is a dearth of direct computations regarding the potential energy surface and rate constants specific to ethers, especially when considering large molecular systems and intricate branched-chain structures. Furthermore, combustion kinetic models for large molecular ethers generally utilize rate constants derived from those of structurally similar alcohols or alkane fuels. Consequently, chemical kinetic studies involve the calculation of energy barriers and rate rules for the intramolecular H-migration reaction class of RIORIIOO· radicals, which are systematically conducted using the isodesmic reaction method (IRM). The geometries of the species participating in these reactions are optimized, and frequency calculations are executed using the M06-X method in tandem with the 6-31+G(d,p) basis set by the Gaussian 16 program. Moreover, the M06-2X/6-31+G(d,p) method acts as the low-level ab initio method, while the CBS-QB3 method is utilized as the high-level ab initio method for calculating single-point energies. Rate constants at the high-pressure-limit are computed based on the reaction class transition state theory (RC-TST) by ChemRate program, incorporating asymmetric Eckart tunneling corrections for intramolecular H-migration reactions across a temperature range of 500 to 2000 K. It was found that the isodesmic reaction method gives accurate energy barriers and rate constants, and the rate constants of the H-migration reaction for RIORIIOO· radicals diverge from those of comparable reactions in alkanes and alcohol fuels. There are significant disparities in energy barriers and rate constants across the entire reaction classes of the H-migration reaction for RIORIIOO· radicals, necessitating the subdivision of the H-migration reaction into subclasses. Rate rules are established by averaging the rate constants of representative reactions for each subclass, which is pivotal for the advancement of accurate low-temperature combustion reaction mechanisms for ethers.

Quasi-boson approximation yields accurate correlation energy in the 2D electron gas

We report the successful adaptation of the quasi-boson approximation, a technique traditionally employed in nuclear physics, to the analysis of the two-dimensional electron gas. We show that the correlation energy estimated from this approximation agrees closely with the results obtained from quantum Monte Carlo simulations. Our methodology comprehensively incorporates the exchange self-energy, direct scattering, and exchange scattering for a particle-hole pair excited out of the mean-field ground state within the equation-of-motion framework. The linearization of the equation of motion leads to a generalized random phase approximation (gRPA) eigenvalue equation whose spectrum indicates that the plasmon dispersion remains unaffected by exchange effects, while the particle-hole continuum experiences a marked upward shift due to the exchange self-energy. Using the gRPA excitation spectrum, we calculate the zero-point energy of the quasi-boson Hamiltonian, thereby approximating the correlation energy of the original Hamiltonian. This research highlights the potential and effectiveness of applying the quasi-boson approximation to the gRPA spectrum, a fundamental technique in nuclear physics, to extended condensed matter systems. Published by the American Physical Society 2024

Toward Reliable Conformational Energies of Amino Acids and Dipeptides─The DipCONFS Benchmark and DipCONL Datasets.

Simulating peptides and proteins is becoming increasingly important, leading to a growing need for efficient computational methods. These are typically semiempirical quantum mechanical (SQM) methods, force fields (FFs), or machine-learned interatomic potentials (MLIPs), all of which require a large amount of accurate data for robust training and evaluation. To assess potential reference methods and complement the available data, we introduce two sets, DipCONFL and DipCONFS, which cover large parts of the conformational space of 17 amino acids and their 289 possible dipeptides in aqueous solution. The conformers were selected from the exhaustive PeptideCS dataset by Andris et al. [ J. Phys. Chem. B 2022, 126, 5949-5958]. The structures, originally generated with GFN2-xTB, were reoptimized using the accurate r2SCAN-3c density functional theory (DFT) composite method including the implicit CPCM water solvation model. The DipCONFS benchmark set contains 918 conformers and is one of the largest sets with highly accurate coupled cluster conformational energies so far. It is employed to evaluate various DFT and wave function theory (WFT) methods, especially regarding whether they are accurate enough to be used as reliable reference methods for larger datasets intended for training and testing more approximated SQM, FF, and MLIP methods. The results reveal that the originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies are not sufficiently accurate. Among the DFT methods tested as an alternative reference level, the revDSD-PBEP86-D4 double hybrid performs best with a mean absolute error (MAD) of 0.2 kcal mol-1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient r2SCAN-3c composite method also shows excellent results, with an MAD of 0.3 kcal mol-1, similar to the best-tested hybrid ωB97M-D4. With these findings, we compiled the large DipCONFL set, which includes over 29,000 realistic conformers in solution with reasonably accurate r2SCAN-3c reference conformational energies, gradients, and further properties potentially relevant for training MLIP methods. This set, also in comparison to DipCONFS, is used to assess the performance of various SQM, FF, and MLIP methods robustly and can complement training sets for those.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

Estimating the Wind Power by Using K-Nearest Neighbors (KNN) and Python

In recent years the production and distribution of renewable energy has become a popular trend in power generation, and renewable energy sources such as hydro, solar, and wind energy are examples of natural and clean energy sources. Among the renewable energy sources, wind energy source is one of the trending and crucial energy sources that has outstanding electricity power generation. This research investigates an estimation model of wind power to address the real-life issues in renewable energy power generation by estimating an accurate amount of wind power production by applying the machine learning (ML) K-Nearest Neighbors (KNN) technique integrated with Python programming. To achieve the expected outcome for this research, it is expected to collect data from wind energy, wind speed forecasts, and all the relevant information that supports the research in analyzing the technique. There are various meteorological methods to evaluate the performance of wind power generation, but in this research, the methodology is to employ machine learning techniques such as KNN integrated with Python coding to achieve estimated wind energy output. Python is a software program and is diversely used for coding various algorithmic formulas to obtain results. In this research, Python is best suited for investigating the performance of the KNN technique for estimating the most accurate result for wind power generation. Here, the report presents the methods of applying the KNN technique and displaying the outcome on screen by a graph representation. The outcome of the research showed that the KNN is a more reliable technique to make an accurate prediction on wind power generation. A similar approach can be implemented in various renewable energy data to estimate an accurate energy output.

Machine Learning for Accurate Energy Analysis of Double Beta Decay Events

Point-contact germanium detectors are used to detect and analyze particles and their decay processes. This research focuses on the search for a phenomenon known as neutrinoless double beta decay, where two neutrons within a nucleus transform into two protons, emitting electrons in the process. Unlike standard double beta decay, no neutrinos are emitted, suggesting that the neutrino may act as its own antiparticle, effectively canceling itself out. Detecting this process would provide crucial insights into the nature of neutrino mass. During such an event, the detector registers a spike in energy due to the emitted electrons, producing a step-like pulse in the output channels. The energy of the event is determined by the difference in charge between the ‘top’ and ‘bottom’ steps of the pulse. However, accurately determining the energy is complicated by the presence of a resistor in the detector, which causes the top step to decay exponentially back to a baseline in preparation for the next event. This decay makes it challenging to determine the true energy of the pulse. My work this summer focused on developing machine learning models to reconstruct the step-like nature of pulses from their decayed versions recorded by the detector. This involved constructing deep neural networks trained on inputs (decayed pulses) and corresponding targets (the original step pulses) to learn to accurately reconstruct the target from the input. Initially, the networks were trained on simulated detector data. Once proficient in reconstruction, electronic noise was added to the input pulses to simulate real-world conditions, under which the models were trained to remove both electronic noise and decay. The models were trained to handle pulses with varying decay constants to improve their generalization ability. These neural networks can now be applied to real detector data for accurate energy analysis of double beta decay events.

Pure rotational and rovibrational spectroscopy of cyclopropylamine in the far-infrared region: -NH2 torsion.

The pure rotational and rovibrational spectra of the ν27 -NH2 torsion of cyclopropylamine (CPA) in the far-infrared region were measured with a high-resolution Fourier transform infrared coupled to a synchrotron. The complex spectra reflect the presence of both trans and gauche conformers. Analysis of the pure rotational spectra (34-64cm-1) yielded accurate rotational and centrifugal distortion constants of the ground and first two torsional excited states of trans-CPA. The fundamental, hot bands and weak overtones were identified and assigned in the 200-550cm-1 range. Global analysis of over 19 000 transitions provides accurate energy levels of the torsional polyads up to vT = 3. The torsional levels and their rotational constants were in agreement with the theoretical results from quasiadiabatic channel reaction path Hamiltonian (RPH) calculations, emphasizing the need for molecular-specific theoretical treatments for large amplitude motions. Tunneling components of the torsional fundamental of gauche-CPA were assigned based on the RPH results and symmetry considerations, differing from previous experimental and theoretical work. This comprehensive spectroscopic characterization of CPA is crucial for its potential detection in the interstellar medium as a precursor to complex prebiotic molecules, providing essential data for future astronomical searches and advancing our understanding of nitrogen-containing organic molecules in space.

Full-dimensional coupled-channel statistical approach to atom-triatom systems and applications to H/D + O3 reaction.

The statistical quantum model (SQM), which assumes that the reactivity is controlled by entrance/exit channel quantum capture probabilities, is well suited for chemical reactions with a long-lived intermediate complex. In this work, a time-independent coupled-channel implementation of the SQM approach is developed for atom-triatom systems in full dimensionality. As SQM treats the capture dynamics quantum mechanically, it is capable of handling quantum effects such as tunneling. A detailed study of the H/D + O3 capture dynamics was performed by applying the newly developed SQM method on an accurate global potential energy surface. Agreement with previous ring polymer molecular dynamics (RPMD) results on the same potential energy surface is excellent except for very low temperatures. The SQM results are also in reasonably good agreement with available experimental rate coefficients. The strong H/D kinetic isotope effect underscores the dominant role of quantum tunneling under an entrance channel barrier at low temperatures.

Calibration of binding energy and clarification of interfacial band bending for the Al2O3/diamond heterojunction

Due to the presence of an intrinsic C 1s peak in diamond, it is impossible to calibrate its binding energies using the adventitious C 1s peak (284.8 eV) during x-ray photoelectron spectroscopy measurement. The absence of accurate binding energy measurement makes it challenging to determine the interfacial band bending for the oxide/diamond heterojunction. To overcome this issue, a net-patterned gold (Au) mask is applied to the boron-doped diamond (B-diamond) to suppress the charge-up effect and calibrate the binding energy using the standard Au 4f peak (83.96 eV). The B-diamond epitaxial layer shows downward band bending toward the surface with a valence band maximum of 0.85 eV. Upon the formation of Al2O3 using an ozone precursor through the atomic layer deposition technique, the B-diamond continues to exhibit downward band bending toward the Al2O3/B-diamond interface. However, the bending energy has reduced, potentially attributed to the modification of the oxygen vacancies on the B-diamond surface by the ozone precursor during the Al2O3 deposition.

Local Second Order Mo̷ller-Plesset Theory with a Single Threshold Using Orthogonal Virtual Orbitals: A Distributed Memory Implementation.

In order to alleviate the computational burden associated with superlinear compute scalings with molecular size in electron correlation methods, researchers have developed local correlation methods that wisely treat relatively small contributions as zeros but still yield accurate energy approximation. Such local correlation techniques can also be combined with parallel computing resources to obtain further efficiency and scalability. This work focuses on the distributed memory parallel implementation of a local correlation method for second order Mo̷ller-Plesset (MP2) theory. This method also only has a single threshold to control the dropping of terms and accuracy of different computing kernels in the algorithm. The process partitioning strategy and distributed parallel implementation with the message passing interface (MPI) are discussed. In particular, the algorithm relies on a fixed sparsity pattern matrix multiplication and a corresponding distributed conjugate gradient solver, which exhibits almost linear scaling in both strong and weak scaling analyses. Numerical experiments on a range of molecules, including linear chains and molecules with 2 and 3-dimensional characters, are reported. For example, with only 32 MPI ranks, this MP2 implementation can calculate the correlation energy of vancomycin in def2-TZVP basis within 0.003% accuracy (10-6.5 threshold) in half an hour, where the same problem is unfeasible to solve with sequential or pure shared memory implementations.

CO2 and NO2 formation on amorphous solid water

Context. The dynamics of molecule formation, relaxation, diffusion, and desorption on amorphous solid water (ASW) is studied in a quantitative fashion. Aims. The formation probability, stabilization, energy relaxation, and diffusion dynamics of CO2 and NO2 on cold ASW following atom+diatom recombination reactions are characterized quantitatively. Methods. Accurate machine-learned energy functions combined with fluctuating charge models were used to investigate the diffusion, interactions, and recombination dynamics of atomic oxygen with CO and NO on ASW. Energy relaxation to the ASW and into water internal degrees of freedom were determined from the analysis of the vibrational density of states. The surface diffusion and desorption energetics were investigated with extended and nonequilibrium MD simulations. Results. The reaction probability is determined quantitatively and it is demonstrated that surface diffusion of the reactants on the nanosecond time scale leads to recombination for initial separations of up to 20 Å. After recombination, both CO2 and NO2 stabilize by energy transfer to water internal and surface phonon modes on the picosecond timescale. The average diffusion barriers and desorption energies agree with those reported from experiments, which validates the energy functions. After recombination, the triatomic products diffuse easily, which contrasts with the equilibrium situation, in which both CO2 and NO2 are stationary on the multinanosecond timescale.

Estimating primary substation boundaries and the value of mapping Great Britain's electrical network infrastructure

Localised data aggregation in many countries including Great Britain (GB) is typically performed at a geographical level with polygon boundaries that have a robust and trusted governance system in place. This means there is confidence in a process to create a set of polygons that have unique identifiers coupled to geographical areas, and the ability to have these updated through a defined code of practice. Examples found across many countries are in the delivery of post, such as post codes and zip codes, and of the definition of census areas and municipal boundaries. The confidence in these boundaries allows different data to be aggregated by third parties, which itself provides greater amounts of data over comparable geographical areas to enhance wider analysis and decision making. As helpful as these polygons are for certain types of analyses, they are not specifically defined for energy systems analysis. Here we combine publicly available datasets published from the six regional electricity Distribution Network Operators of GB to produce a new geospatial dataset with 4436 unique polygons defining the areas served by electrical primary substations. An example demonstrating the value of these polygons is given to link these polygons with postcode level open government datasets on domestic energy consumption (2015–2020) from the Department of Energy Security and Net Zero (DESNZ). The resulting new data reflects energy statistics aggregated to the geographical areas served by each primary electrical substation across Great Britain. The significant value of the generated data is demonstrated by the quantification of the domestic annual heating demand presently met by natural gas across different primary substation areas which varies from an average of 5100 kWh to 28,900 kWh per property. This highlights how the rate of electrical capacity expansion will have to differ by geography under an electrified heating scenario. Therefore, we believe there is a compelling argument for all countries to set up processes to create and update polygons that have a meaningful relationship to energy systems. This would allow more accurate energy systems analysis to be performed, ultimately leading to an accelerated or potentially lower cost transition to a net zero world.