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At least 1,459 records · Page 81

Vibrations and Phase Stability in Mixed Valence Antimony Oxide

α-Sb 2 O 4 (cervantite) and β-Sb 2 O 4 (clinocervantite) are mixed valence compounds with equal proportions of Sb III and Sb V as represented in the formula Sb III Sb V O 4 . Their structure and properties can be difficult to calculate owing to the Sb III lone-pair electrons. Here, we present a study of the lattice dynamics and vibrational properties using a combination of inelastic neutron scattering, Mössbauer spectroscopy, nuclear inelastic scattering, and density functional theory (DFT) calculations. DFT calculations that account for lone-pair electrons match the experimental densities of phonon states. Mössbauer spectroscopy reveals the β phase to be significantly harder than the α phase. Calculations with O vacancies reveal the possibility for nonstoichiometric proportions of Sb III and Sb V in both phases. An open question is what drives the stability of the α phase over the β phase, as the latter shows pronounced kinetic stability and lower symmetry despite being in the high-temperature phase. Since the vibrational entropy difference is small, it is unlikely to stabilize the α phase. In conclusion, our results suggest that the α phase is more stable only because the material is not fully stoichiometric.

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Multielectron Bond Cleavage Processes Enabled by Redox-Responsive Phosphinimide Ligands

The activation of small molecules via multielectron redox processes offers promise in mediating difficult transformations related to energy conversion processes. While molecular systems that engage in one- and two-electron redox processes are widespread, those that participate in the direct transfer of four or more electrons to small molecules are very rare. To that end, we report a mononuclear Cr II complex competent for the 4-electron reduction of dioxygen (O 2 ) and nitrosoarenes. These systems additionally engage in facile two-electron group transfer reactivity, including O atom excision and nitrene transfer. Structural, spectroscopic, and computational studies support bond activation processes that intimately occur at a mononuclear chromium(phosphinimide) center and highlight the unusual structural responsiveness of the phosphinimides in stabilizing a range of metal redox states.

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From +I to +IV, Alkalis to Actinides: Capturing Cations across the Periodic Table with Keggin Polyoxometalate Ligands

Coordination chemistry trends across the periodic table are often difficult to probe experimentally due to limitations in finding a versatile but consistent chelating platform that can accommodate various elements without changing its coordination mode. Herein, we present new metal/ligand systems covering a wide range of ionic radii, charges, and elements. Five different ligands derived from the Keggin structure (HBW 11 O 39 8– , PW 11 O 39 7– , SiW 11 O 39 8– , GeW 11 O 39 8– , and GaW 11 O 39 9– ) were successfully crystallized with six different cations (Na + , Sr 2+ , Ba 2+ , La 3+ , Ce 4+ , and Th 4+ ) and characterized by single-crystal X-ray diffraction. Twenty-five new compounds were obtained by using Cs + as the counterion, yielding a consistent base formula of Cs x [M(XW 11 O 39 ) 2 ]·nH 2 O. Despite having a similar first-coordination sphere geometry (i.e., 8-coordinated), the nature of the central cation was found to impact the long-range geometry of the complexes. This unique crystallographic data set shows that, despite the traditional consensus, the local geometry of the cation (i.e., metal–oxygen bond distance) is not enough to depict the full impact of the complexed metal ion. The bending and twisting of the complexes, as well as ligand–ligand distances, were all impacted by the nature of the central cation. We also observed that counterions play a critical role by stabilizing the geometry of the M(XW 11 ) 2 complex and directing complex–complex interactions in the lattice. We also define certain structural limits for this type of complex, with the large Ba 2+ ion seemingly approaching those limits. Finally, this study thus lays the foundation for capturing the coordination chemistry of other rarer elements across the periodic table such as Ra 2+ , Ac 3+ , Bk 4+ , Cf 3+ , etc.

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Predicting Quantum Criticality in Single-Crystalline Ce 2 Ru 3 Ge 5

Strongly correlated f-electron systems are known to host exotic quantum states, such as quantum criticality, complex order parameters, and unconventional superconductivity. However, the appearance of these exotic states is difficult to predict, making the study of quantum critical behavior challenging, especially in ferromagnetic materials. Herein, we report a structure–property map for Ce 2 M 3 X 5 (M = transition metal; X = main group element) that aids in the targeted design of materials likely to exhibit quantum criticality. Guided by this map, we report on the synthesis of single-crystalline Ce 2 Ru 3 Ge 5 and provide, for the first time, magnetic susceptibility, heat capacity, resistivity, and magnetoresistance measurements on single crystals. Here, we observe a weak ferromagnetic-like response at 7.5 K, which is contrasted with the bulk ferromagnetic ordering that appears in polycrystalline samples. Non-Fermi liquid behavior is seen in the temperature dependent electrical resistivity and heat capacity of the single crystals, suggesting proximity to a ferromagnetic quantum critical point without chemical or physical pressure. Given the contrast with previous reports of polycrystals, these results lead us to propose that single crystalline Ce 2 Ru 3 Ge 5 is intrinsically tuned into the vicinity of a ferromagnetic quantum critical point.

36 MATERIALS SCIENCE↗

Structural Analysis of Curium, Promethium, and Early Lanthanides Using 2,2′:6′,2″-Terpyridine in the Presence of Acid

Trivalent lanthanides and actinides are difficult to separate, often relying on subtle differences in bonding between the 4f– and 5f–orbitals. However, trivalent Cm poses additional complexity owing to its half-filled 5f 7 shell. This configuration causes Cm to have very similar properties to several of the early lanthanides, more specifically, similarly sized Pm. In this study, similarities were probed through isolating an isostructural series between the early lanthanides (La–Eu) and Cm with the oligoamine 2,2′:6′2″-terpyridine in the presence of acid. Single-crystal X-ray diffraction studies highlighted the similarities of Pm and Cm under these conditions and further demonstrated the difficulty in separating these elements. Despite the similarities, differences exist between interactions of Pm and Cm with terpyridine in the solid state. Interestingly, Cm appears to be more similar to Sm than to Pm. Furthermore, this study continues to probe the fundamental chemistry of Pm and Cm with respect to slight differences that can be leveraged in their separation from each other.

Vogt, Trenton B. [Michigan State Univ., East Lansi↗

Regioselective Hydrosilylation Catalysis with Supported Well-Defined Pt(0) Complexes: Effects of Surface Anions and Phosphenium Ligands

Achieving stable, selective single-atom catalysts is challenging because localsurface-site structures are difficult to control. Surface organometallic chemistry and organic−inorganic hybrid materials offer partial solutions, but applications to supporting zero-valent metals are limited. We demonstrate that well-defined, ionically bound N-heterocyclic phosphenium ([NHP] + ) ligands can be generated on silylium-functionalized sulfated zirconia ([ i Pr 3 Si][SZO]). These surface-bound [NHP][SZO] ligands coordinate Pt(0) centers, forming [(NHP)Pt(0)L][SZO] precatalysts that are highly active for alkyne hydrosilylation. Systematic studies reveal that sterically bulky aromatic ligands enhance regioselectivity, achieving performances comparable to molecular Pt catalysts. Further, more-coordinating anions support more-regioselective precatalysts; therefore, SZO supports more-selective species than weaker-coordinating Al(OC(CF 3 ) 3 ) 3 - functionalized silica, a trend confirmed by molecular analogues. These results demonstrate that both the ligand and support control catalytic behavior and enable solid-state structure− activity relationships.

Catalysts↗

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water’s remarkable versatility as a solvent. However, accurately predicting the feasibility of hydrolysis through computational techniques is a difficult task, as subtle changes in reactant structure like heteroatom substitutions or neighboring functional groups can influence the reaction outcome. Furthermore, hydrolysis is sensitive to the pH of the aqueous medium, and the same reaction can have different reaction properties at different pH conditions. In this work, we have combined reaction templates and high-throughput ab initio calculations to construct a diverse data set of hydrolysis free energies. The developed framework automatically identifies reaction centers, generates hydrolysis products, and utilizes a trained graph neural network (GNN) model to predict ΔG values for all potential hydrolysis reactions in a given molecule. The long-term goal of the work is to develop a data-driven, computational tool for high-throughput screening of pH-specific hydrolytic stability and the rapid prediction of reaction products, which can then be applied in a wide array of applications including chemical recycling of polymers and ion-conducting membranes for clean energy generation and storage.

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CHARMM-GUI Bicelle Builder : An Extension of Membrane Builder for Modeling and Simulation of Bicelle Systems

Membrane mimetics, such as detergent micelles, nanodiscs, and amphipol complexes, which can provide membrane-like environments while retaining small and soluble features, have been utilized to study membrane proteins. A bicelle, composed of varying lipids and detergents, is a useful membrane mimetic because the lipid-to-detergent ratio, the q-value, can be adjusted to alter the properties of the aggregate, including the thickness and size of the bicelle. However, building a bicelle model for modeling and simulation studies requires nontrivial efforts, even for experts. We introduce CHARMM-GUI Bicelle Builder, a web-based platform that can generate various all-atom bicelle systems via a graphical user interface with all available lipids and detergents in Membrane Builder. To illustrate and validate Bicelle Builder with practical systems, we have modeled and simulated pure bicelles consisting of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) lipids with 1,2-dihexanoyl-sn-glycero-3-phosphocholine (C6DHPC) detergents and protein–bicelle complexes, composed of DMPC with C6DHPC, foscholine-10 (FOS10), and lysophosphatidylcholine-12 (LPC12) detergents. Our simulation results indicate that Bicelle Builder can generate reliable and robust bicelle models with and without proteins that retain DMPC bilayer characteristics. Bicelle Builder is expected to help researchers better understand not only bicelles themselves but also atomistic-level structures of protein–bicelle complexes that are often difficult to access through experimental approaches.

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Combining Reactive Quantum-Mechanical Molecular-Dynamics Simulations with Mutagenesis, Crystallography, and Enzyme Kinetics to Reveal Plausible Steps of Isocyanide Hydratase Catalysis

A complete understanding of enzyme mechanisms requires atomistic details of chemical reactions. Quantum-based molecular dynamics simulations (QMD) are a potential source of this information, but trade-offs between accuracy and computational cost have limited their use. We previously developed extended Lagrangian Born–Oppenheimer molecular dynamics (XL-BOMD) methods that leverage a negligible compromise in accuracy to substantially decrease the cost of QMD simulations. Here, we develop a reactive QMD approach using the latest XL-BOMD formulation, which enables efficient simulations of highly reactive systems, and use it to investigate mechanisms of intermediate formation in isocyanide hydratase (ICH) catalysis. In QMD simulations, molecular analogs of ICH active site residues reacted with para-nitrophenyl isocyanide, forming a thioimidate. Analysis of simulated atomic configurational and charge dynamics revealed a pathway where protonation of the isocyanide carbon occurs prior to thioimidate formation and suggested a possible role of Asp17 as a proton donor in the early phase of ICH catalysis. To test whether the pathway seen using the reactive QMD approach might be relevant to ICH catalysis, we performed X-ray crystallography and pre-steady-state enzyme kinetics studies of wild-type and D17N mutant ICH. Both the structure and kinetics are sensitive to the D17N mutation in a manner that is consistent with the order of the reaction steps seen in the simulations. Mobile protons play essential roles in many enzymes, yet they are difficult to observe experimentally, making the ordering of proton-dependent steps ambiguous in many enzyme mechanisms. The ability to directly simulate model reactions for the design of experiments that provide information about enzyme mechanisms involving mobile protons demonstrates the significance of our reactive QMD approach and motivates further biological applications.

36 MATERIALS SCIENCE↗

MS25: Materials Science-Focused Benchmark Data Set for Machine Learning Interatomic Potentials

Here, we present MS25, a benchmark data set for evaluating machine learning interatomic potentials (MLIPs) across diverse materials-relevant systems including MgO surfaces, liquid water, zeolites, a catalytic Pt surface reaction, high-entropy alloys (HEAs), and disordered Zr-oxides. Five MLIP architectures (MACE, NequIP, Allegro, MTP, and Torch-ANI) are trained and tested, focusing not only on traditional metrics (energies, forces, and stresses) but also explicitly validating derived physical observables such as lattice constants, volumes, and reaction barriers. We find that most models reach comparable accuracy on standard error metrics across the simple systems, although equivariant MLIPs offer 1.5–2× improvements over nonequivariant MLIPs in energy and force error for structurally complex or compositionally disordered environments such as HEAs and Zr–O systems. Our analysis highlights that low errors in energy and force predictions do not guarantee reliable observables, emphasizing the necessity of explicit validation. We demonstrate limitations in cross-framework transferability, as models trained on one zeolite framework (CHA) fail to reliably generalize to predictions of structurally distinct frameworks (e.g., MFI). Size-extensive tests show some dependence on system size for MgO, resulting from forced periodicity. The HEA and Zr–O data sets are identified as challenging tests for future benchmarks and MLIP model architecture developments as they show significant differentiation in error between MLIP architectures and are still relatively difficult at 1000 training images. Moving forward, we recommend that benchmarking efforts shift their focus from marginal accuracy improvements in energy and force errors toward identifying and understanding model failure modes, rigorously assessing transferability, and evaluating how their errors affect observable predictions. For researchers looking to choose an MLIP architecture, we suggest selecting equivariant MLIP architectures if the complexity of the system is a challenge. For simple materials problems, auxiliary features such as integration with molecular dynamics engines, trade-offs between computational data set generation cost vs MLIP inference speed, and framework integration may play a more important decision factor than small differences in error metrics that are unlikely to matter for production-level research.

chemical structure↗

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Machine-learned interatomic models represent a significant advancement in simulation methods, extending the predictive ability of first-principles methods to previously inaccessible length and time scales. However, the data-driven nature of these models can lead to difficult-to-detect errors that can compromise prediction accuracy. To address this challenge, we introduce a novel fingerprinting approach based on the Chebyshev Interaction Model for Efficient Simulation (ChIMES) ML-IAM graph-based descriptor. Our strategy enables efficient and statistically rigorous analysis of system configurations used in ML-IAM training and those generated by their application, e.g., in molecular dynamics simulations. We demonstrate that these fingerprints can effectively assess novelty of a configuration relative to an existing data set and determine dissimilarity among individual configurations, which are two key tasks in workflows for active learning-based ML-IAM training, data set curation, and on-the-fly uncertainty quantification.

36 MATERIALS SCIENCE↗

Steering Molecular Dynamics Simulations of Membrane-Associated Proteins with Neutron Reflection Results

Herein, we present a novel method to incorporate structural results from surface-sensitive scattering, such as X-ray or neutron reflectometry, into molecular dynamics simulations. While reflectometry techniques generally provide a means to determine the molecular-scale structures of organized interfacial films, they were recently shown to offer the capability to characterize the structures of protein-membrane complexes supported by a solid substrate. One-dimensional information inherent in the experimental results is used in the form of component volume occupancy (CVO) profiles, which describe the distribution of molecular components within an interfacial architecture, to construct real-space constraints in the form of a biasing potential for the simulation that vanishes when the simulated and experimental profiles agree. This approach improves the correspondence between simulation and experiment, as shown in the re-evaluation of an neutron-reflection-derived structure which was approximated by an independent molecular dynamics simulation in earlier work, and it also leads to faster equilibration of ensemble structures. We further show that time averaging the CVO profile that develops in the simulation while biasing with this approach permits fluctuations about the average that are necessary for conformational exploration of the system. This method is particularly valuable for studies of proteins at interfaces that contain disordered regions since the conformation of such regions is difficult to judge from the analysis of one-dimensional experimental profiles and may take prohibitively long to equilibrate in simulations.

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Polishing the Gold Standard: The Role of Orbital Choice in CCSD(T) Vibrational Frequency Prediction

While CCSD(T) with spin-restricted Hartree-Fock (RHF) orbitals has long been lauded for its ability to accurately describe closed-shell interactions, the performance of CCSD(T) on open-shell species is much more erratic, especially when using a spin-unrestricted HF (UHF) reference. Previous studies have shown improved treatment of open-shell systems when a non-HF set of molecular orbitals, like Brueckner or Kohn-Sham density functional theory (DFT) orbitals, is used as a reference. Inspired by the success of regularized orbital-optimized second-order Møller-Plesset perturbation theory (κ-OOMP2) orbitals as reference orbitals for MP3, we investigate the use of κ-OOMP2 orbitals and various DFT orbitals as reference orbitals for CCSD(T) calculations of the corrected ground-state harmonic vibrational frequencies of a set of 36 closed-shell (29 neutrals, 6 cations, 1 anion) and 59 open-shell diatomic species (38 neutrals, 15 cations, 6 anions). The aug-cc-pwCVTZ basis set is used for all calculations. The use of κ-OOMP2 orbitals in this context alleviates difficult cases observed for both UHF orbitals and OOMP2 orbitals. Removing two multireference systems and 12 systems with ambiguous experimental data leaves a pruned data set. Overall performance on the pruned data set highlights CCSD(T) with a B97 orbital reference (CCSD(T):B97), CCSD(T) with a κ-OOMP2 orbital reference (CCSD(T):κ-OOMP2), and CCSD(T) with a B97M-rV orbital reference (CCSD(T):B97M-rV) with RMSDs of 8.48 cm -1 , and 8.50 cm -1 , and 8.75 cm -1 respectively, outperforming CCSD(T):UHF by nearly a factor of 5. Moreover, the performance on the closed- and open-shell subsets shows these methods are able to treat open-shell and closed-shell systems with comparable accuracy and robustness. CCSD(T) with RHF orbitals is seen to improve upon UHF for the closed-shell species, while spatial symmetry breaking in a number of restricted open-shell HF (ROHF) references leads CCSD(T) with ROHF reference orbitals to exhibit the poorest statistical performance of all methods surveyed for open-shell species. The use of κ-OOMP2 orbitals has also proven useful in diagnosing multireference character that can hinder the reliability of CCSD(T).

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Semi-Automated Creation of Density Functional Tight Binding Models through Leveraging Chebyshev Polynomial-Based Force Fields

Density functional tight binding (DFTB) is an attractive method for accelerated quantum simulations of condensed matter due to its enhanced computational efficiency over standard density functional theory (DFT) approaches. However, DFTB models can be challenging to determine for individual systems of interest, especially for metallic and interfacial systems where different bonding arrangements can lead to significant changes in electronic states. In this regard, we have created a rapid-screening approach for determining systematically improvable DFTB interaction potentials that can yield transferable models for a variety of conditions. Our method leverages a recent reactive molecular dynamics force field where many-body interactions are represented by linear combinations of Chebyshev polynomials. This allows for the efficient creation of multi-center representations with relative ease, requiring only a small investment in initial DFT calculations. Here, we have focused our workflow on TiH 2 as a model system and show that a relatively small training set based on unit-cell-sized calculations yields a model accurate for both bulk and surface properties. Our approach is easy to implement and can yield reliable DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

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A Practical Approach to Wave Function Propagation, Hopping Probabilities, and Time Steps in Surface Hopping Calculations

We compare several established approaches for propagating wave functions and calculating hopping probabilities within the fewest switches surface hopping (FSSH) algorithm for difficult cases with many electronic states and many trivial crossings. If only a single time step (Δt c ) is employed, we find that no published approach can accurately capture the dynamics correctly unless Δt c → 0 (which is not computationally feasible). If multiple time steps are employed, for a fixed classical time step (Δt c ), a robust scheme can be found for dynamically choosing quantum time steps (δt q1 and δt q2 ) and calculating hopping probabilities so that one can systematically reduce all errors and achieve maximally efficient accuracy; scattering calculations confirm that one can choose a fairly large classical time step. Furthermore, the robust scheme presented here uses both the “local diabatic” and adiabatic interpolation and thus borrows elements from both the Granucci/Persico and Meek/Levine algorithms. Our findings should be broadly applicable in the future.

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Improving Estimation of the Koopman Operator with Kolmogorov–Smirnov Indicator Functions

It has become common to perform kinetic analysis using approximate Koopman operators that transform high-dimensional timeseries of observables into ranked dynamical modes. The key to the practical success of the approach is the identification of a set of observables that form a good basis on which to expand the slow relaxation modes. Good observables are, however, difficult to identify a priori and suboptimal choices can lead to significant underestimations of characteristic time scales. Leveraging the representation of slow dynamics in terms of Hidden Markov Models (HMM), we propose a simple and computationally efficient clustering procedure to infer surrogate observables that form a good basis for slow modes. Here, we apply the approach to an analytically solvable model system as well as on three protein systems of different complexities. We consistently demonstrate that the inferred indicator functions can significantly improve the estimation of the leading eigenvalues of Koopman operators and correctly identify key states and transition time scales of stochastic systems, even when good observables are not known a priori.

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Predicting the X-ray Absorption Spectrum of Ozone with Single Configuration State Functions

X-ray absorption spectra (XAS) of biradicaloid species are often thought to represent a challenge to theoretical methods. This has led to the testing of recently developed multireference techniques on the XAS of ozone, but reproduction of the experimental spectral profile has proven difficult. We utilize a minimal model consisting of a single configuration state function (CSF) per excited state to model core-level excitations of ozone, with the orbitals of each CSF optimized using the restricted open-shell Kohn–Sham (ROKS) method. This protocol leads to semiquantitative agreement with experimental XAS. In fact, we find that low-lying core-hole excited states in biradicaloids can be approximated with individual CSFs, despite the presence of multireference character in the ground state. Here, we also report that the 1s → π* and 1s → σ* transitions have quite distinct widths for O 3 . This reveals the importance of sampling over a representative range of geometries from the vibrational ground state for properly assessing the accuracy of electronic structure methods against experiments instead of the popular procedure of uniformly broadening stick spectra at the equilibrium geometry.

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Analytic Nuclear Gradients for Complete Active Space Linearized Pair-Density Functional Theory

Accurately modeling photochemical reactions is difficult due to the presence of conical intersections and locally avoided crossings, as well as the inherently multiconfigurational character of excited states. As such, one needs a multistate method that incorporates state interaction in order to accurately model the potential energy surface at all nuclear coordinates. The recently developed linearized pair-density functional theory (L-PDFT) is a multistate extension of multiconfiguration PDFT, and it has been shown to be a cost-effective post-MCSCF method (as compared to more traditional and expensive multireference many-body perturbation methods or multireference configuration interaction methods) that can accurately model potential energy surfaces in regions of strong nuclear–electronic coupling in addition to accurately predicting Franck–Condon vertical excitations. Here, in this paper, we report the derivation of analytic gradients for L-PDFT and their implementation in the PySCF-forge software, and we illustrate the utility of these gradients for predicting ground- and excited-state equilibrium geometries and adiabatic excitation energies for formaldehyde, s-trans-butadiene, phenol, and cytosine.

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