3,297 Search Results

Diamond defects are among the most promising qubits. Modeling their properties through accurate quantum mechanical simulations can further their development into robust units of information. We use the recently developed capped density functional embedding theory (capped-DFET) with the multiconfigurational n-electron valence second-order perturbation theory to characterize the electronic excitation energies for different spin manifolds of the well-characterized negatively charged substitutional N defect adjacent to a vacancy (V_C) in diamond (N_CV_C⁻). We successfully reproduce vertical excitation energies for both triplet and singlet states of N_CV_C⁻ with errors < 0.1 eV. Unlike other embedding methods, capped-DFET exhibits robust predictions that are approximatelymore » independent of the embedded cluster size: it only requires a cluster to contain the defect atoms and their nearest neighbors (as small as a 40-atom capped cluster). Furthermore, our method is free from slowly converging Coulomb interactions between charged defects, and thus also only weakly dependent on supercell size.« less

Direct conversion of captured forms of carbon, or reactive carbon capture (RCC), presents an opportunity to reduce the energy intensity and cost of direct CO₂ utilization from dilute sources. While amine-based sorbents effectively capture CO₂, their use for RCC presents numerous challenges with typical pure metal catalysts used for electrochemical CO₂ reduction (CO₂R). Here, using both theory and experiments, we find that Ni–N–C single atom catalysts are effective for RCC conversion to CO using a diethanolamine sorbent, in contrast to pure metal catalysts. Computational analysis reveals that RCC can proceed directly through direct reduction of the sorbent-CO₂ adduct or indirectlymore » by C–N bond breaking facilitating CO₂ adsorption and subsequent reduction. We find that the latter mechanism is most prevalent at low overpotentials where we experimentally observe RCC selectivity. We also find experimentally that the rate of CO production for RCC with Ni–N–C catalysts can exceed pure bicarbonate solutions at intermediate sorbent concentration (0.1–0.5 M DEA) under dilute (10–25%) streams of CO₂ at low overpotentials. The coordination environment of Ni sites and the solution speciation influence their RCC activity, with changes in protonation to coordinating N/C atoms resulting in changing the RCC mechanism and consequent activity. In situ X-ray absorption spectroscopy and computational analysis reveal restructuring under RCC conditions due to hydrogen coadsorption with DEA that limits the stability of Ni–N–C catalysts. This work highlights the importance of carefully controlling the catalyst and solution environment to achieve active and stable RCC electrocatalysis.« less

Developing neural network potentials (NNPs) accurate under non-equilibrium dynamics is challenging, as such systems require extensive sampling beyond equilibrium phases. Here we construct high-fidelity NNPs for zinc oxide (ZnO), a polymorphic ionic solid, using density functional theory (DFT) reference data. To efficiently capture transitional configurations, we combine enhanced-sampling molecular dynamics with empirical potentials, data distillation, and pretraining on short-range atomic energies (A-Train), followed by transfer learning with DFT-relabeled datasets. This hierarchical approach improves transferability across polymorphs and stress states. We further introduce effective charge separation, treating long-range Coulombic terms analytically while short-range residual interactions are learned by the NNP. Themore » optimal effective charges fall in the range 0.5–1.0 q_e, consistent with dielectric-screened values derived from formal charges but distinct from Bader estimates. Motivated by this observation, we propose a simple data-driven protocol in which effective charges are optimized by comparing DFT reference energies with explicit Coulomb calculations, without additional NNP training. This strategy improves accuracy and transferability in DFT-level predictions of energies, forces, and stress. Together, these results provide a practical charge-selection framework for robust NNP development in ionic solids, enabling reliable simulation of polymorphic phase transformations and non-equilibrium dynamics.« less

Solid-state lithium batteries require safe, robust electrolytes to enable higher energy densities and improved safety over conventional cells. Hybrid polymer–ceramic electrolytes are a promising solution, combining the processability of polymers with the high ionic conductivity and mechanical strength of inorganic fillers. In this work, we demonstrate a solventless, UV-curing method to produce hybrid solid electrolytes using a poly(ethylene glycol) dimethyl ether (PEGDME)-based photocurable matrix incorporating Li_1.5Al_0.5Ge_1.5(PO₄)₃ (LAGP) or Li₇La₃Zr₂O₁₂ (LLZO) ceramic electrolyte. Inorganic filler loadings up to ∼55 wt.% could be successfully incorporated via this process which was the highest inorganic content at which the slurry remains processable and curedmore » into a uniform film. The resulting UV-cured composite electrolytes remain flexible and exhibit room-temperature ionic conductivities on the order of 10⁻⁴ S·cm⁻¹, along with notably improved lithium-ion transference numbers compared to conventional polymer electrolytes. Similar performance and processing limits were observed for both LAGP and LLZO, indicating that ceramic filler chemistry does not significantly affect the UV-curing process or the electrolyte's ion transport properties in this regime. Eliminating solvents from fabrication not only simplifies processing and mitigates environmental concerns but also enables higher solid contents that enhance mechanical strength and help suppress lithium dendrite formation. In conclusion, this scalable approach thus paves the way for manufacturing robust composite solid electrolytes for next-generation solid-state batteries (SSBs).« less

Copper catalyst gas diffusion electrodes (GDEs) have demonstrated unique electrochemical selectivity converting CO₂ to C₂-hydrocarbons such as ethylene and ethanol but have been challenged by their hydrophobic chemical stability and internal electrical resistance leading to low energy efficiency. Carbon-supported GDEs have low electrical resistance but lack sufficient stability at industrially relevant current densities. While polymer-supported GDEs have improved hydrophobicity, they also display high in-plane electrical resistance, particularly at industrial scales. Here, in this work, we demonstrate a composite gas diffusion layer that combines hydrophobic porous polymers with an electrically conductive backbone addressing these core gas diffusion electrode (GDE) scaling challenges.more » We investigate the material properties of standalone porous perfluoropolyether (PFPE) polymers, including porosity and surface morphology, under varying processing conditions and then incorporate these polymers into a porous copper foam. This composite enhances the mechanical rigidity necessary for cell assembly and provides a through-plane electrical conduction path to reduce electrical resistive losses. This enhanced PFPE composite GDE displays efficient CO₂ reduction, achieving 15% ethylene energy efficiency at 100 cm². These findings contribute to the development of advanced catalyst materials and electrode architectures and promote scalable strategies for electrochemical conversion of CO₂ into high-value carbon products.« less

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We foundmore » that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.« less

Raw data for Figure 2 is located in: /Experiments/Fig2_delayed_aggregation Raw data for Figures 3 and 4 can be found at: /Experiments/Fig3_4_Core_shell The analysis code is available at: /Experiments/Code Raw data for the Figure 5 simulation is locate in: /Simulation/Raw_data/Fig5_Structural_features_of_cluster

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 ofmore » 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.« less

The breakdown of solid metal into powder during the hydride-dehydride process is commercially important for the formation of titanium and other metal powders. Typically, the milling of the brittle hydride powder occurs in a ball mill, where milling media impacts powder particles to break them down. The milling media can impact particles that are larger than desired, as desired, or smaller than desired, indiscriminately making all particles smaller. In this work, we investigate how to minimize waste powder production during milling using two different milling methods, planetary ball milling and milling in a sieve shaker (sieve-milling). Both processes yielded similarmore » amounts of 20–75 μm diameter powder (the target size range); however, sieve-milling generated a significantly smaller amount of undersized waste powder. The powders were characterized by X-ray diffraction, SEM/STEM, and magnetic susceptibility. Several differences between ball milling and sieve-milling processes are discussed. We then conclude that the decreased yield of undersized powder in sieve-milling was due to a combination of lower impact energy in sieve-milling, unreacted metallic cores in the hydride flakes, and the ability to mill target particle sizes during sieve-milling. While these results are from the milling of brittle hydride powder, similar methods may be applicable to other brittle powders, including ceramics or salts.« less

Solid solution strengthening (SSS) is widely used to enhance mechanical properties of metals. Originally developed for dilute alloys, classical SSS theories are presently challenged by the rise of complex concentrated alloys (CCA) with nearly equiatomic compositions. Here, we propose and develop a method of “computational alchemy” in which interatomic interactions are modified to systematically vary two key physical parameters defining SSS - atomic size misfit and elastic stiffness misfit - over a maximally wide range of two misfits. The resulting alchemical alloys are subjected to massive (~10⁸ atoms) molecular dynamics (MD) simulations reproducing full complexity of plastic strength response. Atmore » variance with prevailing views, stiffness misfit is observed to contribute to SSS on par if not more than size misfit. Furthermore, depending on exactly how two misfits are combined, they result in synergistic (amplification) or antagonistic (compensation) effect on alloy strengthening. Unlike real CCAs in which each component element comes with its own specific size and stiffness, our alchemical model alloys span the space of two misfits continuously revealing trends in alloy strengthening unrecognized so far. Our study demonstrates unique value of intentionally unrealistic models for gaining deep physical insights into material behaviors that are difficult to reveal otherwise.« less