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Raman Spectroscopy Coupled with Chemometric Analysis for Speciation and Quantitative Analysis of Aqueous Phosphoric Acid Systems
Complex chemical systems that exhibit varied and matrix-dependent speciation are notoriously difficult to monitor and characterize on-line and in real-time. Optical spectroscopy is an ideal tool for in situ characterization of chemical species that can enable quantification as well as species identification. Chemometric modeling, a multivariate method, has been successfully paired with optical spectroscopy to enable measurement of analyte concentrations even in complex solutions where univariate methods such as Beer’s law analysis fail. Here, Raman spectroscopy is used to quantify the concentration of phosphoric acid and its three deprotonated forms during a titration. In this system, univariate approaches would be difficult to apply due to multiple species being present simultaneously within the solution as pH is varied. Locally Weighted Regression (LWR) modeling was used to determine phosphate concentration from spectral signature. LWR results, in tandem with Multivariate Curve Resolution modeling, provide direct measurement of the concentration of each phosphate species using only the Raman signal. Furthermore, results are presented within the context of fundamental solution chemistry, including Pitzer equations to compensate for activity coefficients and non-idealities associated with high ionic strength systems.
Graph-based quantum response theory and shadow Born–Oppenheimer molecular dynamics
Graph-based linear scaling electronic structure theory for quantum-mechanical molecular dynamics simulations [A. M. N. Niklasson et al., J. Chem. Phys. 144, 234101 (2016)] is adapted to the most recent shadow potential formulations of extended Lagrangian Born–Oppenheimer molecular dynamics, including fractional molecular-orbital occupation numbers [A. M. N. Niklasson, J. Chem. Phys. 152, 104103 (2020) and A. M. N. Niklasson, Eur. Phys. J. B 94, 164 (2021)], which enables stable simulations of sensitive complex chemical systems with unsteady charge solutions. The proposed formulation includes a preconditioned Krylov subspace approximation for the integration of the extended electronic degrees of freedom, which requires quantum response calculations for electronic states with fractional occupation numbers. For the response calculations, we introduce a graph-based canonical quantum perturbation theory that can be performed with the same natural parallelism and linear scaling complexity as the graph-based electronic structure calculations for the unperturbed ground state. Further, the proposed techniques are particularly well-suited for semi-empirical electronic structure theory, and the methods are demonstrated using self-consistent charge density-functional tight-binding theory both for the acceleration of self-consistent field calculations and for quantum-mechanical molecular dynamics simulations. Graph-based techniques combined with the semi-empirical theory enable stable simulations of large, complex chemical systems, including tens-of-thousands of atoms.
Accurate Fe–He machine learning potential for studying He effects in BCC-Fe
Nanostructured Ferritic Alloys (NFAs) containing oxide nanoparticles are prospective advanced structural materials in future fusion reactors. Lacking fusion neutron sources with an adequate flux, modeling is critical to understand radiation damage in the NFAs, including helium effects. Machine learning interatomic potentials (MLPs) have been demonstrated to be capable of describing atomic interactions in chemically complex systems with comparable accuracy to density functional theory (DFT) but with much less computational costs. Hence, MLPs are chosen to study He bubble accumulation in chemically complex NFAs. As a preliminary step, we develop a Fe–He potential, based on ~10,000 atomic configurations. The developed MLP accurately predicts the bulk properties of BCC-Fe compared to DFT, including the lattice constant (2.832 Å), elastic constants (c 11 = 271 GPa, c 12 = 141, c 44 = 93), and phonon frequencies (maximum error < 3.5%). The MLP predicts He binding energies in He n V bubbles and He n clusters with a mean absolute error (MAE) of only ~70 meV, which is ~3 to ~5 times smaller than the MAE of binding energies estimated using empirical potentials. The MLP also accurately predicts the migration barrier of interstitial He (64 meV). Furthermore, the MLP correctly predicts the maximum number of He atoms (critical size) at which a He n V bubble (He n cluster) can kick-out a self-interstitial atom (SIA), forming a bigger He n V 2 bubble (He n V bubble). Subsequently, we use the MLP to perform molecular dynamics simulations to investigate the effect of temperature on the critical size of He n V bubbles and He n clusters. We find that the critical size decreases with increasing temperature — indicating that smaller bubbles and clusters can kick-out an SIA at higher temperatures, thereby escalating the He bubble growth. We report the current Fe–He MLP can be further developed to include all chemical interactions in NFAs.
Predicting short-range order evolution in WTaCrVHf refractory high-entropy alloys
Short-range order (SRO) in multicomponent concentrated alloys affects their mechanical response. Hence, is paramount to understand how composition modifies the chemical ordering in the system to design materials with optimal properties. Here, in this work, we present a methodology to predict the SRO and thermodynamic properties in chemically complex systems and apply it to the WTaCrVHf quinary alloy. We observe that the addition of Hf significantly modifies the SRO, mainly at intermediate to low temperatures, matching experimental observations.
Excitons: Energetics and spatiotemporal dynamics
The concept of an exciton as a quasiparticle that represents collective excited states was originally adapted from solid-state physics and has been successfully applied to molecular aggregates by relying on the well-established limits of the Wannier exciton and the Frenkel exciton. However, the study of excitons in more complex chemical systems and solid materials over the past two decades has made it clear that simple concepts based on Wannier or Frenkel excitons are not sufficient to describe detailed excitonic behavior, especially in nano-structured solid materials, multichromophoric macromolecules, and complex molecular aggregates. In addition, important effects such as vibronic coupling, the influence of charge-transfer (CT) components, spin-state interconversion, and electronic correlation, which had long been studied but not fully understood, have turned out to play a central role in many systems. This has motivated new experimental approaches and theoretical studies of increasing sophistication. This article provides an overview of works addressing these issues that were published for A Special Topic of the Journal of Chemical Physics on ``Excitons: Energetics and spatio-temporal dynamics" and discusses their implications.
Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics
Multivariate regression models were optimized for the quantification of sulfuric acid (H 2 SO 4 ) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH 4 ) 2 SO 4 [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H 2 SO 4 . The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, in this study, partial least squares regression models were optimized for the quantification of H 2 SO 4 and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H 2 SO 4 . The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.
Large-Scale Interlaboratory DI-FT-ICR MS Comparability Study Employing Various Systems
We report ultra-high resolution mass spectrometry (UHR-MS) coupled with direct infusion (DI) electrospray ionization offers a fast solution for accurate untargeted profiling. Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometers have been shown to produce a wealth of insights into complex chemical systems because they enable unambiguous molecular formula assignment even if the vast majority of signals is of unknown identity. Inter-laboratory comparisons are required to apply this type of instrumentation in quality control (for food industry or pharmaceutics), large-scale environmental studies or clinical diagnostics. Extended comparisons employing different FT-ICR MS instruments with qualitative direct infusion analysis are scarce since the majority of detected compounds cannot be quantified. The extent to which observations can be reproduced by different laboratories remains unknown. We set up a preliminary study which encompassed a set of seventeen laboratories around the globe, diverse in instrumental characteristics and applications, to analyze the same sets of extracts from commercially available standard human blood plasma and Standard Reference Material (SRM) for blood plasma (SRM1950), which were delivered at different dilutions or spiked with different concentrations of pesticides. The aim of this study was to assess the extent to which the outputs of differently tuned FTICR mass spectrometers, with different technical specifications, are comparable for setting the frames of a future DI-FT-ICR MS ring trial. We concluded that a cluster of five laboratories, with diverse instrumental characteristics showed comparable and representative performance across all experiments, setting a reference to be used in a future ring trial on blood plasma.
Probing Complex Chemical Processes at the Molecular Level with Vibrational Spectroscopy and X-ray Tools
Understanding the origins of structure and bonding at the molecular level in complex chemical systems spanning magnitudes in length and time is of paramount interest in physical chemistry. Here, we have coupled vibrational spectroscopy and X-ray based techniques with a series of microreactors and aerosol beams to tease out intricate and sometimes subtle interactions, such as hydrogen bonding, proton transfer, and noncovalent interactions. This allows for unraveling the self-assembly of arginine-oleic acid complexes in an aqueous solution and growth processes in a metal–organic framework. Terahertz and infrared spectroscopy provide an intimate view of the hydrogen-bond network and associated phase changes with temperature in neopentyl glycol. The hydrogen-bond network in aqueous glycerol aerosols and levels of protonation of nicotine in aqueous aerosols are visualized. Future directions in probing the hydrogen-bond networks in deep eutectic solvents and organic frameworks are described, and we suggest how X-ray scattering coupled to X-ray spectroscopy can offer insight into the reactivity of organic aerosols.
Uncertainty Quantification and Sensitivity Analysis of Low-Dimensional Manifold via Co-Kurtosis PCA in Combustion Modeling
For multi-scale multi-physics applications e.g., the turbulent combustion code Pele, robust and accurate dimensionality reduction is crucial to solving problems at exascale and beyond. A recently developed technique, Co-Kurtosis based Principal Component Analysis (CoK-PCA) which leverages principal vectors of co-kurtosis, is a promising alternative to traditional PCA for complex chemical systems. To improve the effectiveness of this approach, we employ Artificial Neural Networks for reconstructing thermo-chemical scalars, species production rates, and overall heat release rates corresponding to the full state space. Our focus is on bolstering confidence in this deep learning based non-linear reconstruction through Uncertainty Quantification (UQ) and Sensitivity Analysis (SA). UQ involves quantifying uncertainties in inputs and outputs, while SA identifies influential inputs. One of the noteworthy challenges is the computational expense inherent in both endeavors. To address this, we employ the Monte Carlo methods to effectively quantify and propagate uncertainties in our reduced spaces while managing computational demands. Our research carries profound implications not only for the realm of combustion modeling but also for a broader audience in UQ. By showcasing the reliability and robustness of CoK-PCA in dimensionality reduction and deep learning predictions, we empower researchers and decision-makers to navigate complex combustion systems with greater confidence.
Real-Time Time-Dependent Density Functional Theory for Simulating Nonequilibrium Electron Dynamics
The explicit real-time propagation approach for time-dependent density functional theory (RT-TDDFT) has increasingly become a popular first-principles computational method for modeling various time-dependent electronic properties of complex chemical systems. Here, in this Perspective, we provide a nontechnical discussion of how this first-principles simulation approach has been used to gain novel physical insights into nonequilibrium electron dynamics phenomena in recent years. Following a concise overview of the RT-TDDFT methodology from a practical standpoint, we discuss our recent studies on the electronic stopping of DNA in water and the Floquet topological phase as examples. Our discussion focuses on how RT-TDDFT simulations played a unique role in deriving new scientific understandings. We then discuss existing challenges and some new advances at the frontier of RT-TDDFT method development for studying increasingly complex dynamic phenomena and systems.
A Synchrotron-Based Vacuum Ultraviolet Photoionization Mass Spectrometer-Coupled Microreactor To Probe Thermocatalysis
Vacuum ultraviolet photoionization (VUV-PI) mass spectrometry offers an isomer-selective and universal ionization with minimal fragmentation detection of organics in complex chemical systems such as pyrolysis and combustion. Here, in this study, we report a state-of-the-art experimental setup of a universal catalytic microreactor combined with a molecular beam to investigate the thermocatalytic oxidation of a heterogeneous system relevant for probing reactions at gas–solid interfaces. In strong contrast to traditional off-line analytical methods, this technique is capable of identifying and quantifying short-lived species (radicals) as well as stable products to decipher initial reaction steps via the detection of nascent products. The thermocatalytic oxidative degradation of exo-tetrahydrodicyclopentadiene (JP-10), a high energy-density hydrocarbon fuel, over solid titanium–aluminum–boron reactive mixed metal nanopowder (Ti-Al-B RMNP) is exploited to showcase potential applications. Overall, some 59 nascent gas-phase products are identified via photoionization efficiency (PIE) curves, including oxygenated species and hydrocarbons comprising closed-shell molecules and radicals. The critical temperature for complete oxidative decomposition of JP-10 was lowered by 450 K from 1400 K to 950 K, indicating an efficient thermocatalytic action of Ti-Al-B nanoparticles on JP-10. The enabling of a universal chemical microreactor along with VUV-PI mass spectrometry broadens the applicability of this technique to hydrocarbon fuel oxidation and pyrolysis characterization. This isomer-selective sensitive probing along with the detection of radical transients makes the aforementioned technique superior to other conventional analytical techniques such as microflow tube and pyrolysis-gas chromatography coupled with mass spectrometry for investigating similar pyrolysis reactions and comprehensive quantification.
A Perspective on Sustainable Computational Chemistry Software Development and Integration
The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.
Molecular Symmetry in VQE: A Dual Approach for Trapped-Ion Simulations of Benzene
Understanding complex chemical systems—such as biomolecules, catalysts, and novel materials—is a central goal of quantum simulations. Near-term strategies hinge on the use of variational quantum eigensolver (VQE) algorithms combined with a suitable ansatz. However, straightforward application of many chemically-inspired ansatze yields prohibitively deep circuits. In this work, we employ several circuit optimization methods tailored for trapped-ion quantum devices to enhance the feasibility of intricate chemical simulations. The techniques aim to lessen the depth of the unitary coupled cluster with singles and doubles (uCCSD) ansatz's circuit compilation, a considerable challenge on current noisy quantum devices. Furthermore, we use symmetry-inspired classical post-selection methods to further refine the outcomes and minimize errors in energy measurements, without adding quantum overhead. Our strategies encompass optimal mapping from orbital to qubit, term reordering to minimize entangling gates, and the exploitation of molecular spin and point group symmetry to eliminate redundant parameters. The inclusion of error mitigation via post-selection based on known molecular symmetries improves the results to near milli-Hartree accuracy. These methods, when applied to a benzene molecule simulation, enabled the construction of an 8-qubit circuit with 69 two-qubit entangling operations, pushing the limits for variational quantum eigensolver (VQE) circuits executed on quantum hardware to date. 1 1 This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-000R22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan(https://energy.gov/doe-public-access-plan).
MCCCS-MN
The MCCCS‒MN (Monte Carlo for Complex Chemical Systems‒Minnesota) software is developed by the Siepmann research group at the University of Minnesota. MCCCS‒MN allows for the simulation of multi-component molecular systems in the canonical, isobaric-isothermal (including constant stress for solids), grand-canonical, semi-grand, and Gibbs (NVT, NPT, and more than two simulation boxes) ensembles. It uses the configurational-bias Monte Carlo method to efficiently sample phase space for linear, branched and cyclic chain molecules, the adiabatic nuclear and electronic sampling Monte Carlo method to treat many-body polarization effects, and the aggregation-volume-bias Monte Carlo algorithm to efficiently sample the spatial distribution of associating molecules. MCCCS-MN employs a molecular representation of the system where force fields contain bonded and non-bonded terms. Funding for the development of MCCCS-MN through grants from the National Science Foundation (simulation of fluid phase equilibria and chromatography) and the Department of Energy (simulation of adsorption equilibria) is gratefully acknowledged.
Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation
Interfaces play a pivotal role in dictating the performance and reliability of all-solid-state batteries (ASSBs), where complex electro-chemo-mechanical phenomena at grain boundaries (GBs) and interfaces can lead to degradation and failure. Traditional atomistic simulation methods, such as first-principles calculations and classical molecular dynamics, face limitations in modeling these interfaces due to either high computational cost or insufficient transferability to the diverse atomic environments evolving at interfaces. Machine-learning interatomic potentials (MLIPs) have emerged as a transformative approach, enabling large-scale, high-accuracy simulations of disordered and chemically complex systems by leveraging the predictability of machine learning models trained on first-principles data. Recent applications of MLIPs have demonstrated their ability to capture intricate behaviors at ASSB interfaces, including ion transport, interfacial evolution, and degradation mechanisms, with accuracy and efficiency unattainable by conventional methods. This prospective paper presents comprehensive analysis and practical guidance for MLIP development for GBs and interfaces in ASSBs, with a focus on three key pillars: data generation, model selection, and validation. Here, we review the current state of MLIP applications for GBs and interfaces in both general and ASSB-specific materials, highlighting best practices and challenges in constructing diverse and representative datasets, choosing appropriate machine learning architectures, and rigorously validating model performance. We also discuss emerging strategies and opportunities for improved reliability and efficiency of MLIPs to simulate realistic interfaces in ASSBs.
Understanding the Chemical Complexity of Multicomponent Systems: Uranium Polyoxometalates as Nanosorbents
The overarching objective of this project was to develop a molecular-scale understanding of chemical interactions of the transuranic (TRU) elements with uranyl-based polyoxometalate nanoclusters (UPOMs). The specific goals that supported this overarching objective were to: (i) quantify TRU interactions with UPOMs as a function of TRU, carbonate, and UPOM concentration, pH, and temperature; (ii) examine the molecular-level bonding environment of TRU-UPOM complexes; and (iii) develop a rate expression capable of describing the sorption and reduction of TRU by UPOMs. The published literature suggests several potential interactions can occur in TRU-UPOM systems: (i) encapsulation of TRU and coordination with the internal ‘-yl’ oxygens of the UPOM, (ii) coordination of TRU with the external ‘-yl’ oxygens of the UPOM, and (iii) coordination of TRU with the bridging ligands (e.g., peroxo, hydroxo, oxalate) of the UPOM. The latter two may induce aggregation of the UPOMs. The concept of encapsulation is demonstrated by the X-ray diffraction structure of crystallized U 20 , which reveals a Na + cation under each pentagonal face (Sigmon et al., 2009). Computational modeling shows that, while the negative charge of U 20 is spread among all the oxygen atoms, the internal ‘-yl’ oxygens host the most negative charge (Miro and Bo, 2012). This negative charge is reduced through the complexation of the templating sodium ions in the interior of the UPOM. Furthermore, Nyman et al. (2011) shows that cesium can exit through the pentagonal window of U 28 in the aqueous phase, suggesting that the TRU elements, which are smaller than cesium, may be capable of entering a UPOM through these windows in an exchange reaction. Under this project, we focused on hafnium, neptunium, and plutonium interactions with UPOMs.
Capturing Ring Opening in Photoexcited Enolic Acetylacetone upon Hydrogen Bond Dissociation by Ultrafast Electron Diffraction
Photoinduced biological and chemical reactions are often based on key structural transformations of a molecule driven across multiple electronic states. Acetylacetone (AcAc) is a prototypical system for complex chemical pathways involving several conical intersections (CI) and singlet–triplet intersystem crossings (ISC) characterized by distinct geometries. In the gas phase, AcAc is predominantly in a planar ring-like enolic form stabilized by a strong intramolecular O–H···O hydrogen bond. Following excitation into the S 2 (ππ*) state at 266 nm, acetylacetone undergoes rapid internal conversion followed by intersystem crossing. Such relaxation pathways are associated with structural changes including ring opening, deplanarization, and bond elongation. In this work, ultrafast electron diffraction (UED) at the SLAC MeV-UED setup is employed as a direct structural probe with a time resolution of 160 fs. Together with trajectory surface hopping simulations, analysis of the UED data provides a new perspective on the early time nuclear dynamics in acetylacetone. Specifically, AcAc is observed to undergo ring opening, deplanarization, and bond elongation all within the first 700 fs after photoexcitation. The monitored dynamics is associated mainly with the nuclear motion on the S 1 potential energy surface, formed after very rapid transfer from S 2 to S 1 , allowing AcAc to reach the conical intersection to intersystem crossing. Such time scales of nuclear motion are contrasted with the time scales of electronic transitions in AcAc that were previously characterized with spectroscopic methods, specifically internal conversion (<100 fs) and intersystem crossing (∼1.5 ps).