A5053233284- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Computational Drug Discovery Methods
- Advanced Chemical Physics Studies
- Catalytic Processes in Materials Science
- Spectroscopy and Quantum Chemical Studies
- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Electron and X-Ray Spectroscopy Techniques
- X-ray Spectroscopy and Fluorescence Analysis
- Carbon Nanotubes in Composites
- Electronic and Structural Properties of Oxides
- Graphene research and applications
- Solid-state spectroscopy and crystallography
- Nanopore and Nanochannel Transport Studies
- Mesoporous Materials and Catalysis
- Zeolite Catalysis and Synthesis
- Catalysis and Oxidation Reactions
- Semiconductor materials and devices
- ZnO doping and properties
- Electrocatalysts for Energy Conversion
- Quantum Dots Synthesis And Properties
- Surface and Thin Film Phenomena
- Various Chemistry Research Topics
- Molecular Junctions and Nanostructures
Brookhaven National Laboratory
2016-2025
Binghamton University
2024
Center for Functional Nanomaterials
2022
Upton Hospital
2016-2020
National Institute of Standards and Technology
2018
University of California, Davis
2008-2010
IBM Research - Almaden
2008
University of Illinois Urbana-Champaign
2002-2006
Virginia Tech
2002
Arizona State University
2002
Fast-charging batteries typically use electrodes capable of accommodating lithium continuously by means solid-solution transformation because they have few kinetic barriers apart from ionic diffusion. One exception is titanate (Li4Ti5O12), an anode exhibiting extraordinary rate capability apparently inconsistent with its two-phase reaction and slow Li diffusion in both phases. Through real-time tracking Li+ migration using operando electron energy-loss spectroscopy, we reveal that facile...
Tracking the structure of heterogeneous catalysts under operando conditions remains a challenge due to paucity experimental techniques that can provide atomic-level information for catalytic metal species. Here we report on use X-ray absorption near-edge (XANES) spectroscopy and supervised machine learning (SML) refining 3D geometry catalysts. SML is used unravel hidden relationship between XANES features catalyst geometry. To train our method, rely ab initio simulations. Our approach allows...
We describe an ab initio approach to compute the optical absorption spectra of molecules and solids, which is suitable for study large systems gives access within a wide energy range. In this approach, quantum Liouville equation solved iteratively first order perturbation theory, with Hamiltonian containing static self-energy operator. This procedure equivalent solving statically screened Bethe-Salpeter equation. Explicit calculations single particle excited states inversion dielectric...
Intermolecular interactions in the van der Waals bonded benzene crystal are studied from first principles, by combining exact exchange energies with correlation defined adiabatic connection fluctuation-dissipation theorem, within random phase approximation. Correlation evaluated using an iterative procedure to compute eigenvalues of dielectric matrices, which eliminates computation unoccupied electronic states. Our results for structural and binding properties solid very good agreement...
Abstract High‐nickel cathodes attract immense interest for use in lithium‐ion batteries to boost Li‐storage capacity while reducing cost. For overcoming the intergranular‐cracking issue polycrystals, single‐crystals are considered an appealing alternative, but aggravating concerns on compromising ionic transport and kinetic properties. We report here a quantitative assessment of redox reaction single‐crystal LiNi 0.8 Mn 0.1 Co O 2 using operando hard X‐ray microscopy/spectroscopy, revealing...
The geometry and bonding of N-heterocyclic carbenes to metal surfaces depends on the substituents N-atoms.
Simulations of excited state properties, such as spectral functions, are often computationally expensive and therefore not suitable for high-throughput modeling. As a proof principle, we demonstrate that graph-based neural networks can be used to predict the x-ray absorption near-edge structure spectra molecules quantitative accuracy. Specifically, predicted reproduce nearly all prominent peaks, with 90% peak locations within 1 eV ground truth. Besides its own utility in analysis inference,...
Single atom catalysts (SACs) have shown high activity and selectivity in a growing number of chemical reactions. Many efforts aimed at unveiling the structure–property relationships underpinning these activities developing synthesis methods for obtaining SACs with desired structures are hindered by paucity experimental capable probing attributes local structure, electronic properties, interaction support—features that comprise key descriptors their activity. In this work, we describe...
X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that sensitive to structural and electronic properties. First-principles simulated XAS has been widely used as a powerful tool interpret experimental spectra draw physical insights. Recently, there also growing interest in building computational databases enable data analytics machine learning applications. However, are nontrivial differences among commonly simulation codes, both underlying...
As most biological species, photosynthetic lifeforms have evolved to function optimally, despite thermal disorder and with fault tolerance. It remains a challenge understand how this is achieved. To address challenge, the of protein−pigment complex photosystem I (PSI) cyanobacterium Synechococcus elongatus investigated theoretically. The recently obtained high-resolution structure exhibits an aggregate 96 chlorophylls that are electronically coupled as light-harvesting antenna complex. This...
The eigenvalues and eigenvectors of the dielectric matrix $ϵ$ provide a compact representation screening properties interacting electronic systems. We have previously shown that eigenvalue spectrum may be efficiently computed by iterative linear-response calculations for nonmetallic systems obtained through an eigenvalue-eigenvector decomposition where only small number are included. Here we investigate spectral matrices variety (solids, nanostructures, molecules) as well convergence...
Four-electron oxygen reduction reaction (4e-ORR), a key pathway in energy conversion, is preferred over the two-electron that falls short dissociating dioxygen molecules. Gold surfaces exhibit high sensitivity of ORR to its atomic structures. A long-standing puzzle remains unsolved: why Au with {100} sub-facets were exceptionally capable catalyze 4e-ORR alkaline solution, though limited within narrow potential window. Herein we report discovery dominant whole range on {310} surface...
Zero-strain electrodes, such as spinel lithium titanate (Li4/3Ti5/3O4), are appealing for application in batteries due to their negligible volume change and extraordinary stability upon repeated charge/discharge cycles. On the other hand, this same property makes it challenging probe structural changes during electrochemical reaction. Herein, we report situ studies of lithiation-driven transformations Li4/3Ti5/3O4 via a combination X-ray absorption spectroscopy ab initio calculations. Based...
As machine learning (ML) methods continue to be applied a broad scope of problems in the physical sciences, uncertainty quantification is becoming correspondingly more important for their robust application. Uncertainty-aware have been used select applications, but largely scalar properties. In this work, we showcase an exemplary study which neural network ensembles are predict x-ray absorption spectra small molecules, as well pointwise uncertainty, from local atomic environments. The...
Electrochemical degradation of solid electrolytes is a major roadblock in the development solid-state batteries. Combining X-ray absorption spectroscopy characterization, first-principles simulations, and machine learning, here we report atomic-scale oxidative mechanisms sulfide using Li3PS4 (LPS) as model system. The begins with decrease Li neighbor affinity to S atoms, followed by formation S-S bonds PS4 tetrahedron deforms. After first cycle, motifs become strongly distorted, PS3 start...
Abstract Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet inverse prediction precise from spectroscopic data remains a formidable challenge. In this study, we introduce framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional disordered systems, using amorphous carbon ( -C) model system. work, new based on diffusion model, recent...
A structure-based description of excitation migration in multireaction center light harvesting systems is introduced. The an extension the sojourn expansion, which decomposes terms repeated detrapping and recapture events. approach applied to trimeric form cyanobacterial photosystem I (PSI). Excitation found be shared between PSI monomers chlorophylls providing strongest respective links are identified. sharing investigated by computing cross-monomer trapping probabilities. It seen that on...
We present a first-principles study of the static dielectric properties ice and liquid water. The eigenmodes matrix $ϵ$ are analyzed in terms maximally localized functions similar, their definition, to Wannier orbitals obtained from Bloch eigenstates electronic Hamiltonian. show that lowest ${ϵ}^{\ensuremath{-}1}$ real space can be separated into groups related screening lone pairs, intra-, intermolecular bonds, respectively. local conveniently exploited build approximate matrices for...
X-ray absorption near-edge structure (XANES) spectroscopy is a robust and element-specific tool for probing the atomic of materials. Traditional methods work in forward direction by simulating XANES spectra from models. Here, authors present opposite: computational method predicting local structural geometry which so-called inverse problem solved using supervised machine learning. The robustness fidelity are demonstrated an average 86% classification accuracy K-edge hundreds materials across...
Abstract X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of absorber atom. In this work, we develop database sulfur K-edge XAS spectra crystalline and amorphous lithium thiophosphate based on atomic structures reported in Chem. Mater ., 34, 6702 (2022). The simulations using excited electron core-hole pseudopotential approach implemented Vienna Ab initio Simulation Package. Our contains...
Improved understanding of structural and chemical properties through local experimental probes, such as X-ray absorption near-edge structure (XANES) spectroscopy, is crucial for the design functional materials. In recent years, significant advancements have been made in development data science approaches automated interpretation XANES structure–spectrum relationships. However, existing studies primarily focused on crystalline solids small molecules, while fewer efforts devoted to disordered...
Accurate classification of molecular chemical motifs from experimental measurement is an important problem in physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) 41 different on small molecules simulated C, N, O K-edge X-ray absorption near-edge structure (XANES) spectra. Our not only achieve class-balanced accuracies more than 0.95 but also accurately quantify uncertainty. We show that including multiple...