Principles Simulations Research Articles

Prediction challenge: First principles simulation of the ultrafast electron diffraction spectrum of cyclobutanone.

Computer simulation has long been an essential partner of ultrafast experiments, allowing the assignment of microscopic mechanistic detail to low-dimensional spectroscopic data. However, the ability of theory to make a priori predictions of ultrafast experimental results is relatively untested. Herein, as a part of a community challenge, we attempt to predict the signal of an upcoming ultrafast photochemical experiment using state-of-the-art theory in the context of preexisting experimental data. Specifically, we employ abinitio Ehrenfest with collapse to a block mixed quantum-classical simulations to describe the real-time evolution of the electrons and nuclei of cyclobutanone following excitation to the 3s Rydberg state. The gas-phase ultrafast electron diffraction (GUED) signal is simulated for direct comparison to an upcoming experiment at the Stanford Linear Accelerator Laboratory. Following initial ring-opening, dissociation via two distinct channels is observed: the C3 dissociation channel, producing cyclopropane and CO, and the C2 channel, producing CH2CO and C2H4. Direct calculations of the GUED signal indicate how the ring-opened intermediate, the C2 products, and the C3 products can be discriminated in the GUED signal. We also report an a priori analysis of anticipated errors in our predictions: without knowledge of the experimental result, which features of the spectrum do we feel confident we have predicted correctly, and which might we have wrong?

Comparison of SiGeC/SiGe/SiC–Si heterojunction based vertical nanowire FET using non equilibrium Green's function

Advancement in FET technology has forced researchers to seek alternate semiconductor devices and materials. TFETs, junctionless FETs, and nanowires are a few examples of modern FETs, whereas Si alloys, graphene, and TMDC are a few materials that have shown promising behaviour to replace Si in the future. In this work, SiGeC/SiGe/SiC–Si based vertical heterostructure nanowires have been investigated. Due to the nanoscale dimensions of the FET and to incorporate the quantum effects along with Schrodinger-Poisson equations, non-equilibrium Green's function has been used as a principle simulator. For simulation, Ge/Carbon mole concentration of 0.25 and 0.01, respectively is used. To thoroughly investigate the effect of variation in doping profile and temperature range, simulated heterojunction nanowire showed that SiGeC-based nanowire result in a better ION/IOFF ratio as compared to SiGe and SiC-based heterojunction nanowire.

The generation of noise-like pulses and various solitons with CuO nanorods as a broadband saturable absorber

Copper oxide (CuO) nanorods exhibit unique photoelectric properties due to their high surface area, excellent volume effect, and quantum confinement effect, which have also attracted wide attention in the application of ultrafast photonics. In the present work, CuO nanorods were successfully prepared by a simple synthesis strategy and characterized in detail. The density of electron states (DOS) and the band structure of CuO were predicted by the first principles simulation method. The nonlinear optical response parameters of CuO nanorods were determined by a twin-balanced detector system. Based on the excellent nonlinear optical response, CuO nanorods were exploited as saturable absorbers (SAs) to achieve the noise-like pulse (NLP) mode-locking operation in the erbium (Er)-doped fiber laser (EDFL) at 1552 nm and ytterbium (Yb)-doped fiber laser (YDFL) at 1038.5 nm. Additionally, by shortening the cavity length of EDFL and finely optimizing the intracavity polarization, various solitons, and dual-wavelength solitons were also observed. Moreover, the dynamic evolution of spectra and pulses in YDFL was simulated using scalar nonlinear Ginzburg-Laudau equation (GLE). Our work well demonstrated the broadband and high-efficiency saturable absorption in CuO nanorods, paving the path for investigating multi-soliton dynamics and broadband ultrafast photonics.

Enhancement of light emission of a low-resistivity silicon nanocrystal thin film: A simulational and experimental study

A silicon nanocrystal (Si NC) white light-emitting (λ = ∼400–900 nm) thin film with a relatively low resistivity of 1.6 × 104 Ω m has been prepared as an active medium for electrically driven Si light sources. The average size of Si NC is 2.4 ± 0.4 nm. To enhance the light emission efficiency of this low-resistivity Si NC thin film, approach of hydrogen passivation suitable for the traditional high-resistivity (1.2 × 107 Ω m in this work for example) red light-emitting Si NC thin film has been tried and found unavailable unfortunately. Our first principles simulation shows that Si NCs bonded to −O, −NH2, −OH, and −H ligands are responsible for red, green, and blue (RGB) primary color emissions in this white light-emitting sample, respectively. Passivation of the sample in NH3 and H2O atmosphere is then conducted, aiming to increase the number of the RGB light emitters. The light emission is significantly enhanced, with photoluminescence intensity, photoluminescence quantum yield, electroluminescence intensity, and net optical gains increased by factors of 4.6, 4.2, 4.0, and ∼3.0, respectively, after 10-day passivation. Further enhancements are expected for longer passivation.

Effect of epoxy groups and defects on the interfacial properties of RGO/Ti composites using the first principles simulation

To develop high-performance titanium/graphene oxide/titanium (Ti/GO/Ti) matrix composites, the effects of the number of epoxy groups and the type of defects on the interfacial properties of the composite models were investigated. The increase of epoxy groups on GO resulted in the enhancement of the interfacial adhesion work but the decrease of the interfacial energy between GO and Ti groups, which was conducive to enhancing interfacial bonding strength. Both single vacancy defects and topological defects favor the interfacial bonding between GO and Ti groups at one and two epoxy groups, and double vacancy defects have a negative effect when there are two epoxy groups. The interaction between C and Ti atoms is strongest at double vacancy defects (single vacancy defects), followed by topological defects, and weakest atoms at single vacancy defects (double vacancy defects) when one (two) epoxy groups are present. The defects cause the integrity of the C atoms in GO to be significantly broken, creating stronger interactions at the interface. The peaks of the d orbitals of Ti atoms in the Ti/GO2/Ti composite model under the three types of defects are reduced compared to pure Ti/GO2/Ti but increased compared to pure Ti/GO1/Ti.

Understanding the ensemble effect in photoenzyme photophysics and reactivity via first principles simulations and machine learning

Understanding the ensemble effect in photoenzyme photophysics and reactivity via first principles simulations and machine learning

Diborides of Multielement Transition Metals: Methods for Calculating Physical and Mechanical Characteristics

From the first principles simulation (using the method of “a priori pseudopotential” and the “quasi-harmonic approximation” method- author’s developments), the basic characteristics of diborides and diborides of multielement transition metals (DMTMs) with an AlB2 type structure were calculated. For both diborides and DMTMs, the linear coefficients of thermal expansion (LCTE) along the axial axes differ little from each other, i.e., transition metal diborides and hexagonal lattice DMTMs are quasi-isotropic. Quasi-isotropy makes it possible to estimate the LCTE using an analytical formula that depends on the melting temperature. In the absence of experimental data on the melting point of DMTMs, a method for calculating it from first principles is presented. The theoretical hardness values of transition metal diborides and DMTMs with averaged parameters were calculated from the first principles. The hardness of both bulk and nano-sized DMTMs was assessed using a hybrid method. There is agreement between the calculated and available experimental data.

Reassessing the transport properties of fluids: A symbolic regression approach.

The viscosity and thermal conductivity coefficients of the Lennard-Jones fluid are extracted through symbolic regression (SR) techniques from data derived from simulations at the atomic scale. This data-oriented approach provides closed form relations that achieve fine accuracy when compared to well-established theoretical, empirical, or approximate equations, fully transparent, with small complexity and high interpretability. The novelty is further outlined by suggesting analytical expressions for estimating fluid transport properties across the whole phase space, from a dilute gas to a dense liquid, by considering only two macroscopic properties (density and temperature). In such expressions, the underlying physical mechanisms are reflected, while, at the same time, it can be a computationally efficient alternative to costly in time and size first principle and/or molecular dynamics simulations.

A Review on the Driving Mechanism of the Spring Algal Bloom in Lakes Using Freezing and Thawing Processes

Spring algal blooms in mid–high-latitude lakes are facing serious challenges such as earlier outbreaks, longer duration, and increasing frequency under the dual pressure of climate warming and human activities, which threaten the health of freshwater ecosystems and water security. At present, the freeze-thaw processes is the key to distinguishing spring algal blooms in mid- to high-latitude lakes from low-latitude lakes. Based on the visualization and an analysis of the literature in the WOS database during 2007–2023, we clarified the driving mechanism of the freeze-thaw process (freeze-thaw, freeze-up, and thawing) on spring algal bloom in lakes by describing the evolution of the freeze-thaw processes on the nutrient migration and transformation, water temperature, lake transparency and dissolved oxygen, and physiological characteristics of algae between shallow lakes and deep lakes. We found that the complex phosphorus transformation process during the frozen period can better explain the spring-algal-bloom phenomenon compared to nitrogen. The dominant species of lake algae also undergo transformation during the freeze-thaw process. On this basis, the response mechanism of spring algal blooms in lakes to future climate change has been sorted out. The general framework of “principles analysis, model construction, simulation and prediction, assessment and management” and the prevention strategy for dealing with spring algal bloom in lakes have been proposed, for which we would like to provide scientific support and reference for the comprehensive prevention and control of spring algal bloom in lakes under the freezing and thawing processes.

The EFG Rosetta Stone: translating between DFT calculations and solid state NMR experiments.

We present a comprehensive study on the best practices for integrating first principles simulations in experimental quadrupolar solid-state nuclear magnetic resonance (SS-NMR), exploiting the synergies between theory and experiment for achieving the optimal interpretation of both. Most high performance materials (HPMs), such as battery electrodes, exhibit complex SS-NMR spectra due to dynamic effects or amorphous phases. NMR crystallography for such challenging materials requires reliable, accurate, efficient computational methods for calculating NMR observables from first principles for the transfer between theoretical material structure models and the interpretation of their experimental SS-NMR spectra. NMR-active nuclei within HPMs are routinely probed by their chemical shielding anisotropy (CSA). However, several nuclear isotopes of interest, e.g.7Li and 27Al, have a nuclear quadrupole and experience additional interactions with the surrounding electric field gradient (EFG). The quadrupolar interaction is a valuable source of information about atomistic structure, and in particular, local symmetry, complementing the CSA. As such, there is a range of different methods and codes to choose from for calculating EFGs, from all-electron to plane wave methods. We benchmark the accuracy of different simulation strategies for computing the EFG tensor of quadrupolar nuclei with plane wave density functional theory (DFT) and study the impact of the material structure as well as the details of the simulation strategy. Especially for small nuclei with few electrons, such as 7Li, we show that the choice of physical approximations and simulation parameters has a large effect on the transferability of the simulation results. To the best of our knowledge, we present the first comprehensive reference scale and literature survey for 7Li quadrupolar couplings. The results allow us to establish practical guidelines for developing the best simulation strategy for correlating DFT to experimental data extracting the maximum benefit and information from both, thereby advancing further research into HPMs.

Two-dimensional BiSbTeX2 (X = S, Se, Te) and their Janus monolayers as efficient thermoelectric materials.

Today, there is a huge need for highly efficient and sustainable energy resources to tackle environmental degradation and energy crisis. We have analyzed the electronic, mechanical and thermoelectric (TE) characteristics of two-dimensional (2D) BiSbTeX2 (X = S, Se and Te) and Janus BiSbTeXY (X/Y = S, Se and Te) monolayers by implementing first principles simulations. These monolayers' dynamic stability and thermal stability have been demonstrated through phonon dispersion spectra and ab initio molecular dynamics (AIMD) simulations, respectively. The band structure of these monolayers can be tuned by applying uniaxial and biaxial strains. The investigated lattice thermal conductivity (κl) for these monolayers lies between 0.23 and 0.37 W m-1 K-1 at 300 K. For a more precise calculation of the scattering rate, we implemented electron-phonon coupling (EPC) and spin-orbit coupling effects to calculate the transport properties. For p(n)-type carriers, the power factor of these monolayers is predicted to be as high as 2.08 × 10-3 W m-1 K-2 and (0.47 × 10-3 W m-1 K-2) at 300 K. The higher thermoelectric figure of merit (ZT) of p-type carriers at 300 K is obtained because of their very low value of κl and high power factor. Our theoretical investigation predicts that these monolayers can be potential candidates for fabricating highly efficient thermoelectric power generators.

System Identification Techniques for Soft Sensors and Multiphase Flow Metering

Production rates estimation and forecasting is a complex problem in multiphase flow systems, such as oil wells. In the oil and gas industry, this task is normally performed by first principles simulation models, which are costly to maintain and adjust. An alternative to this process could be black-box models based on historical data to perform this task. This work proposes to perform an extensive evaluation of existing regressors to create a black-box system identification model to predict oil well liquid flow rates. Improvement strategies such as the creation of error models, time series decomposition, and the use of current-time data are also evaluated. The final results demonstrated the importance of pressure and temperature data for the well flow rate determination and identified the use of current-time data as the best tested technique to improve the performance of the final model.

A 3d convolutional neural network autoencoder for predicting solvent configuration changes in condensed phase biomass reactions

Self-supervised 3d CNN autoencoder combined with a distance-based classifier is trained on first principles simulations to predict the extent of solvent configuration changes during reactive chemical transformations.

Unveiling the mechanism of CO2 electroreduction to C1 and C2 products of ordered double transition metal MXenes

First principles simulations to understand the electrocatalytic CO2RR activity of double transition metal MXenes from an electronic structure point of view.

First principles simulations of MgO(100) surface hydration at ambient conditions.

Developing a better understanding of water ordering and hydroxylation at oxide mineral surfaces is important across a breath of application spaces. Recent vibrational sum frequency generation (vSFG) measurements on MgO(100) surfaces at ambient conditions showed that water dissociates and hydroxylates the surface yielding a non-hydrogen bonded hydroxyl species. Starting from previously determined water hydroxylation patterns on MgO(100), we performed ab initio thermodynamic calculations and vibrational analysis to compare with the vSFG observations. At ambient conditions (i.e., T = 298.15 K and pH2O = 32 mbar), the most thermodynamically favorable surface hydroxylation is found to be p(3 × 2) - 8H2O, involving a dissociation of 25% of the adsorbed water. Analysis of the vibrational density of states for this hydroxylation configuration yielded three different hydrogen bonding environments with the frequency of the peaks in very good agreement with the vSFG measurements. However, the non-H-bonded spectral feature on this surface is predicted to be similar to that expected for Mg(OH)2, a thermodynamically downhill alteration of the surface that must be independently ruled out before one can be fully confident in the apparent theory/vSFG agreement. Our study provides more insights into the ordering and structure of water monolayer at MgO(100) surface at ambient conditions and completes previous theoretical and experimental analysis performed at low temperature and ultra-high vacuum conditions.

Unveiling The Mechanism of The Dendrite Nucleation and Growth in Aqueous Zinc Ion Batteries

AbstractAqueous zinc ion batteries (ZIBs) exhibit great potential for next‐generation energy storage devices. However, significant challenges exist, including the uncontrollable formation of Zn dendrite and side reactions during zinc stripping and plating. The mechanism of Zn dendrite nucleation has yet to be fully understood. In this work, the first principles simulations are used to investigate the Zn dendrite formation process. The unintentionally adsorbed O2− and OH− ions are the inducing factors for Zn dendrite nucleation and growth on the Zn (0001) plane due to significantly increased Zn diffusion barriers. A top‐down method is demonstrated to suppress the dendrite using delaminated V2CTx to capture O2− and OH− ions thanks to reduced Zn diffusion barriers. The experimental results revealed significantly suppressed Zn dendrite nucleation and growth, resulting in a layer‐by‐layer deposit/stripping of Zn. Based on the electrochemical evaluations, the V2CTx‐coated Zn composite delivers a high coulombic efficiency of 99.3% at 1.0 mAh cm−2. Furthermore, the full cell achieves excellent cyclic performance of 93.6% capacity retention after 2000 cycles at 1 A g−1. This strategy has broad scalability and can be widely applied in designing metallic anodes for rechargeable batteries.

Facile band gap tuning in graphene–brucite heterojunctions

The zero band gap of pure graphene is a well-known issue that limits some specific applications of graphene in opto- and microelectronics. This led to several research studies in the so‐called van der Waals composites (known as heterostructures, or heterojunctions), where two monolayers of different materials are stacked and held together by dispersive interactions. In this paper, we introduced and considered a single layer of brucite Mg(OH)2, an overlooked 2D material that can be easily produced by exfoliation (like graphene from graphite), for the creation of the heterojunction. First principles simulations showed that brucite/graphene composites can modify the electronic properties (position of the Dirac cone with respect to the Fermi level and band gap) according to the crystallographic stacking and the presence of point defects. The present work represents then an important step forward in understanding and finding new ways to design two-dimensional materials with tailored electronic and physical properties.

MOCVD of ZrN: Tuning Thin Film Properties Towards Catalytic Applications

Transition metal nitrides (TMN) have gained increasing attention over recent years as active materials for energy related applications including catalysis and energy storage. They feature high electrical conductivities, wide optical band gaps and superior chemical stability.[1] In particular, ZrN has been recently identified as a promising material for a range of potential applications that include plasmonic devices,[2] as a catalyst for the oxygen reduction reaction (ORR),[3] and as a catalyst for the nitrogen reduction reaction (NRR),[4] due to its appealing electronic properties, while being cheap and abundant compared to commonly used noble metals.[1,5] The fabrication of ZrN on large scale under moderate process conditions, with the required properties for catalytic applications, motivates the use of chemical vapor deposition (CVD) as the method of choice. Moreover, with the variation of process parameters that includes precursor choice and growth temperature, it is possible to tune the surface features as well as composition of the layers that are suitable for catalytic applications.In this study, we have employed metal organic chemical vapor deposition (MOCVD) to synthesize ZrN layers on Si and glassy carbon (GC) substrates in the temperature range of 550 – 850 °C. The influence of the nature of the precursor and deposition temperature on the structure, composition and surface morphology of the ZrN layers was systematically studied. Oriented ZrN layers with facetted grain morphology were obtained. ZrN films of different thickness were analyzed by complementary methods including X-ray diffraction (XRD) (Figure 1 a)), Raman spectroscopy, Rutherford backscattering spectrometry (RBS) (Figure 1 b)), nuclear reaction analysis (NRA), scanning electron microscopy (SEM) (Figure 1 c)), and X-ray photoelectron spectroscopy (XPS). Based on the detailed film analysis, catalyst surfaces were modeled by DFT calculations. The NRR activity and selectivity towards the competing hydrogen evolution reaction (HER) were evaluated by first principles simulations and electrochemical experiments. This preliminary study on material fabrication and theoretical investigations lays a foundation for the investigation of ZrN for NRR applications for future studies.

Transition Metal Nitrides for the Electrocatalysis of the Electrochemical Ammonia Synthesis

The direct electrochemical ammonia synthesis (EAS) from water and nitrogen using renewable energy shows great potential for future decentralized production of indispensable green ammonia and may, at least partially, supersede the fossil fuel-based Haber-Bosch process in the future [1]. Material innovation for the conversion of nitrogen to ammonia via the nitrogen reduction reaction (NRR) is one cornerstone for the successful development of the EAS technology. To date, only Lithium-mediated NRR has been demonstrated to show sufficient production rate and faradaic efficiency for a prospective application [2], while the reaction in aqueous electrolyte, independent of the studied material class, suffers from false-positive results due to low productions rates, low Faradaic efficiencies and complex contamination issues [3].Several catalyst systems are currently investigated for NRR in aqueous electrolytes. Transition metal nitrides (TMN) may offer an energetic advantage as electrocatalyst, because the NRR is expected to be catalyzed via the Mars-van-Krevelen mechanism (MvK) on TMNs. Here the protonation of a lattice nitrogen atom forms the first ammonia molecule. The resulting N-vacancy is filled by molecular nitrogen where one N adatom is subsequently protonated to form the second ammonia molecule. Thus, the N2 activation step occurs at the N-vacancy site and avoids the surface adsorption and dissociation of N2. Specific facets of Zr, Cr, V and Nb were described as stable, active and selective TMN catalysts for the NRR based on theoretical catalyst screening [4]. However, current literature studies state contradictory results and require the differentiation of genuine activity and non-catalytic decomposition of TMN catalysts [5], which is currently lacking.In our work, we address the synthesis of Zr-based TMNs, the electrochemical evaluation in a gas diffusion electrode setup and the trace analysis of ammonia by ion chromatography [6]. Only consideration of all three interconnected steps can give an indication of genuine NRR activity, where structural characterization after NRR experiments elucidates the stability of the material. Special interest is given to the role of surface oxynitrides and their ratio to the nitride phase for electrocatalytic NRR. Comparison of nanoparticulate catalysts to surface engineered thin film catalyst as well as insights from first principles simulation delivers a comprehensive characterization of the electrosynthesis of ammonia by TMNs.

Hybrid analytical surrogate-based process optimization via Bayesian symbolic regression

Modular chemical process simulators are widespread in chemical industries to design and optimize production processes with sufficient accuracy. However, convergence issues and entrapment in local optima during process optimization are still challenges to overcome. To circumvent them, surrogate models of first principles simulations have attracted attention as they are easier to handle, with hybrid surrogates combining data-driven surrogate models with mechanistic equations becoming particularly appealing. In this context, this work explores the use of Bayesian symbolic regression to construct and globally optimize hybrid analytical surrogate models of process flowsheets, where some units are approximated with tailored analytical expressions rather than with neural networks or Gaussian processes, which might be harder to globally optimize. Comparing with other prevalent black-box surrogate modeling & optimization approaches, such as kriging and Bayesian optimization, we find that our approach can find better solutions than those identified with pure black-box methodologies, yet model building is much more computationally demanding.