Sequence Blockiness Controls the Structure of Polyampholyte Necklaces
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SEARCH · Search NASA
Results for “Analytical mathematics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
..delta..-Learning of High-Fidelity Electronic Structure Using Graph Neural Networks with Modified Node-Level Features
In this work, we present a ..delta..-learning approach for predicting the eigenvalues calculated with the hybrid functional HSE06 (..epsilon..nkHSE) for a set of metal and nitrogen doped graphene catalysts (MNCs) from Perdew-Burke-Ernzerhof (PBE) inputs. The model presented here incorporates electronic scalar features along with structural information in a graph neural network (GNN). In particular, the PBE eigenvalues for different bands and k-points and orbital-resolved projectors are combined with the applied potential as node-level features along with structural information within the Atomistic Line Graph Neural Network (ALIGNN) architecture. These features enable flexibility for systems with electrified interfaces, such as in electrocatalysts and achieves mean absolute error (MAE) of less than 0.1 eV. The machine learning model reported here achieves a strong generalization to left-out adsorbates (MAE = 0.074 eV) and leave-one-chemical-space-out (MAE = 0.08 eV) and completely left-out metals (MAE = 0.072 eV), confirming the robustness of the machine learning (ML) model in predicting ..epsilon..nkHSE.
36 MATERIALS SCIENCE↗
Revealing the Chemical Bonding in Adatom Arrays via Machine Learning of Hyperspectral Scanning Tunneling Spectroscopy Data
The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and unsupervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the Co 3 Sn 2 S 2 cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the Co 3 Sn 2 S 2 surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Extracting Thin Film Structures of Energy Materials Using Transformers
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Optimizing Desalination Operations for Energy Flexibility
Despite the value of energy optimization in desalination processes, modeling dynamic operations for monthly billing periods has remained a computational challenge. This work proposes a framework for energy flexibility optimization, which includes new modeling features for independent operation of parallel skids, start-up delays associated with chemical stabilization, the consideration of industrial energy tariff structures, and inclusion of hourly electrical carbon intensities. This is done using a modular and computationally efficient formulation that guarantees a globally optimal solution with standard optimization solvers. In this study, the approach is demonstrated in two distinct case studies: a seawater desalination plant in Santa Barbara, CA, and an indirect potable reuse facility in San Jose, CA. Trends predicted from the model are validated against operational facility measurements from a demand response shutdown event. Preliminary results show that optimizing energy flexibility can result in 18.51% monthly cost savings over energy efficiency-optimized operation. The value extracted from a facility-wide shutdown during peak electricity price hours is hampered by start-up delays in post-treatment chemical stabilization. In cases in which a facility does not have much excess capacity, using a flow equalization tank or operating over a wide recovery range may be cost-effective.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Modulating Chemical Environments of Metal–Organic Framework-Supported Molybdenum(VI) Catalysts for Insights into the Structure–Activity Relationship in Cyclohexene Epoxidation
Solid supports are crucial in heterogeneous catalysis due to their profound effects on catalytic activity and selectivity. However, elucidating the specific effects arising from such supports remains challenging. We selected a series of metal–organic frameworks (MOFs) with 8-connected Zr 6 nodes as supports to deposit molybdenum(VI) onto to study the effects of pore environment and topology on the resulting Mo-supported catalysts. As characterized by X-ray absorption spectroscopy (XAS) and single-crystal X-ray diffraction (SCXRD), we modulated the chemical environments of the deposited Mo species. For Mo-NU-1000, the Mo species monodentately bound to the Zr 6 nodes were anchored in the microporous c-pore, but for Mo-NU-1008 they were bound in the mesopore of Mo-NU-1008. Both monodentate and bidentate modes were found in the mesopore of Mo-NU-1200. Cyclohexene epoxidation with H 2 O 2 was probed to evaluate the support effect on catalytic activity and to unveil the resulting structure–activity relationships. SCXRD and XAS studies demonstrated the atomically precise structural differences of the Mo binding motifs over the course of cyclohexene epoxidation. No apparent structural change was observed for Mo-NU-1000, whereas the monodentate mode of Mo species in Mo-NU-1008 and the monodentate and bidentate Mo species in Mo-NU-1200 evolved to a new bidentate mode bound between two adjacent oxygen atoms from the Zr 6 node. This work demonstrates the great advantage of using MOF supports for constructing heterogeneous catalysts with modulated chemical environments of an active species and elucidating structure–activity relationships in the resulting reactions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Nitrogen Out-of-Plane (NOOP) Mechanism for Imine-Based Light-Driven Molecular Motors
Light-driven molecular motors have generated considerable interest due to their potential applications in material and biological systems. Recently, Greb and Lehn reported a new class of molecular motors, chiral N-alkyl imines, which undergo unidirectional rotation induced by light and heat. The mechanism of unidirectional motion in molecular motors containing a C═N group has been assumed to consist of photoinduced torsion about the double bond. In this work, we present a computational study of the photoisomerization dynamics of a chiral N-alkyl imine motor. We find that the location and energetics of minimal energy conical intersections (MECIs) alone are insufficient to understand the mechanism of the motor. Furthermore, a key part of the mechanism consists of out-of-plane distortions of the N atom (followed by isomerization about the double bond). Dynamic effects and out-of-plane distortions are critical to understand the observed (rather low) quantum yield for photoisomerization. Finally, our results provide hints as to how the photoisomerization quantum yield might be increased, improving the efficiency of this class of molecular motors.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Tetracene Diacid Aggregates for Directing Energy Flow toward Triplet Pairs
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Alkali-Metal Interlocking of 2D V 4 O 10 Sheets Defines Discretized Interlayer Shear Relationships
Low-dimensional materials manifest structural anisotropy, quantum confinement, and tightly bound excitonic states, which make them attractive building blocks that can be assembled within three-dimensional laterally stitched heterostructures, stacked van der Waals solids, and complex moiré superlattices. Ion intercalation in the galleries between layered materials provides a means of modifying interlayer separation and coupling, but it is also known to drive the shearing of the layers. In this article, we explore the distinct ligand coordination environments afforded by vanadyl oxygens of singular [V 4 O 10 ] sheets and examine how the size, polarizability, and stoichiometry of Group I cations sandwiched between such layers determine the interlocking of the sheets in stacked structures. Based on the topochemical insertion of alkali-metal ions into the layered λ-V 2 O 5 , we identify seven types of guest ion coordination sites discretized into four distinct regimes of interlayer shear in units of half octahedral widths. The coordination preferences of intercalated cations govern how they interlock 2D [V 4 O 10 ] sheets and engender specific shear conformations. We present evidence that static and dynamic disorder in guest ion arrangement modulate the magnetic structure of the intercalated compounds based on electrostatic polarization, localization of charge and spin density, and lattice distortion. The results illustrate the use of topochemical ion insertion to modulate stacking relationships and magnetic transition characteristics.
36 MATERIALS SCIENCE↗
A data-independent acquisition-based global phosphoproteomics system enables deep profiling
Phosphoproteomics can provide insights into cellular signaling dynamics. To achieve deep and robust quantitative phosphoproteomics profiling for minute amounts of sample, we here develop a global phosphoproteomics strategy based on data-independent acquisition (DIA) mass spectrometry and hybrid spectral libraries derived from data-dependent acquisition (DDA) and DIA data. Benchmarking the method using 166 synthetic phosphopeptides shows high sensitivity (<0.1 ng), accurate site localization and reproducible quantification (~5% median coefficient of variation). As a proof-of-concept, we use lung cancer cell lines and patient-derived tissue to construct a hybrid phosphoproteome spectral library covering 159,524 phosphopeptides (88,107 phosphosites). Based on this library, our single-shot streamlined DIA workflow quantifies 36,350 phosphosites (19,755 class 1) in cell line samples within two hours. Application to drug-resistant cells and patient-derived lung cancer tissues delineates site-specific phosphorylation events associated with resistance and tumor progression, showing that our workflow enables the characterization of phosphorylation signaling with deep coverage, high sensitivity and low between-run missing values.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Automated exploitation of the big configuration space of large adsorbates on transition metals reveals chemistry feasibility
Mechanistic understanding of large molecule conversion and the discovery of suitable heterogeneous catalysts have been lagging due to the combinatorial inventory of intermediates and the inability of humans to enumerate all structures. Here, we introduce an automated framework to predict stable configurations on transition metal surfaces and demonstrate its validity for adsorbates with up to 6 carbon and oxygen atoms on 11 metals, enabling the exploration of ~10 8 potential configurations. It combines a graph enumeration platform, force field, multi-fidelity DFT calculations, and first-principles trained machine learning. Clusters in the data reveal groups of catalysts stabilizing different structures and expose selective catalysts for showcase transformations, such as the ethylene epoxidation on Ag and Cu and the lack of C-C scission chemistry on Au. Deviations from the commonly assumed atom valency rule of small adsorbates are also manifested. This library can be leveraged to identify catalysts for converting large molecules computationally.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Ultrahigh pressure compaction-resistant thin film crosslinked composite reverse osmosis membranes
In this study, we present a class of thin-film crosslinked (TFX) composite reverse osmosis (RO) membranes that resist physical compaction at ultrahigh pressures (up to 200 bar). Since RO membranes experience compaction at virtually all pressure ranges, the ability to resist compaction has widespread implications for RO membrane technology. The process described herein involves crosslinking a phase inverted porous polyimide (PI) support membrane followed by interfacial polymerization of a polyamide layer, thereby forming a fully thermoset composite membrane structure. We explore a range of phase inversion membrane formation parameters such as PI concentration, solvent-cosolvent ratios, coagulation bath composition, and crosslinking methods in addition to interfacial polymerization reaction chemistry and conditions. Overall, TFX membranes exhibit significantly less compaction compared to hand-cast and commercial high-pressure RO membranes, experiencing less than 10% decline in water permeance and maintaining salt rejection over 99% for NaCl solutions up to 180,000 mg/L with 200 bar applied pressure.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Unleashed from constrained optimization: quantum computing for quantum chemistry employing generator coordinate inspired method
Hybrid quantum-classical approaches offer potential solutions to quantum chemistry problems, yet they often manifest as constrained optimization problems. Here, we explore the interconnection between constrained optimization and generalized eigenvalue problems through the Unitary Coupled Cluster (UCC) excitation generators. Inspired by the generator coordinate method, we employ these UCC excitation generators to construct non-orthogonal, overcomplete many-body bases, projecting the system Hamiltonian into an effective Hamiltonian, which bypasses issues such as barren plateaus that heuristic numerical minimizers often encountered in standard variational quantum eigensolver (VQE). Diverging from conventional quantum subspace expansion methods, we introduce an adaptive scheme that robustly constructs the many-body basis sets from a pool of the UCC excitation generators. This scheme supports the development of a hierarchical ADAPT quantum-classical strategy, enabling a balanced interplay between subspace expansion and ansatz optimization to address complex, strongly correlated quantum chemical systems cost-effectively, setting the stage for more advanced quantum simulations in chemistry.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel
Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries and electrocatalysts. Experimental characterizations of such materials by operando microscopy produce rich image datasets, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
High-throughput dataset of impurity adsorption on common catalysts in biomass upgrading applications
Abstract An extensive dataset consisting of adsorption energies of pernicious impurities present in biomass upgrading processes on common catalysts and support materials has been generated. This work aims to inform catalyst and process development for the conversion of biomass-derived feedstocks to fuels and chemicals. A high-throughput workflow was developed to execute density functional theory calculations for a diverse set of atomic (Al, B, Ca, Cl, Fe, K, Mg, Mn, N, Na, P, S, Si, Zn) and molecular (COS, H 2 S, HCl, HCN, K 2 O, KCl, NH 3 ) species on 35 unique surfaces for transition-metal (Ag, Au, Co, Cu, Fe, Ir, Ni, Pd, Pt, Re, Rh, Ru) and metal-oxide (Al 2 O 3 , MgO, anatase-TiO 2 , rutile-TiO 2 , ZnO, ZrO 2 ) catalysts and supports. Approximately 3,000 unique adsorption geometries and corresponding adsorption energies were obtained.
09 BIOMASS FUELS↗
Reduction of the molecular hamiltonian matrix using quantum community detection
Abstract Quantum chemistry is interested in calculating ground and excited states of molecular systems by solving the electronic Schrödinger equation. The exact numerical solution of this equation, frequently represented as an eigenvalue problem, remains unfeasible for most molecules and requires approximate methods. In this paper we introduce the use of Quantum Community Detection performed using the D-Wave quantum annealer to reduce the molecular Hamiltonian matrix in Slater determinant basis without chemical knowledge. Given a molecule represented by a matrix of Slater determinants, the connectivity between Slater determinants (as off-diagonal elements) is viewed as a graph adjacency matrix for determining multiple communities based on modularity maximization. A gauge metric based on perturbation theory is used to determine the lowest energy cluster. This cluster or sub-matrix of Slater determinants is used to calculate approximate ground state and excited state energies within chemical accuracy. The details of this method are described along with demonstrating its performance across multiple molecules of interest and bond dissociation cases. These examples provide proof-of-principle results for approximate solution of the electronic structure problem using quantum computing. This approach is general and shows potential to reduce the computational complexity of post-Hartree–Fock methods as future advances in quantum hardware become available.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The performance of ensemble-based free energy protocols in computing binding affinities to ROS1 kinase
Optimization of binding affinities for compounds to their target protein is a primary objective in drug discovery. Herein we report on a collaborative study that evaluates a set of compounds binding to ROS1 kinase. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to rank the binding free energies. The predicted binding free energies from ESMACS simulations show good correlations with experimental data for subsets of the compounds. Consistent binding free energy differences are generated for TIES and ESMACS. Although an unexplained overestimation exists, we obtain excellent statistical rankings across the set of compounds from the TIES protocol, with a Pearson correlation coefficient of 0.90 between calculated and experimental activities.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
cDVAE: VAE-guided diffusion for particle accelerator beam 6D phase space projection diagnostics
Not Available
43 PARTICLE ACCELERATORS↗