A5036691276- Machine Learning in Materials Science
- Chalcogenide Semiconductor Thin Films
- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Quantum Dots Synthesis And Properties
- X-ray Diffraction in Crystallography
- Crystallization and Solubility Studies
- Electron and X-Ray Spectroscopy Techniques
- Advanced Semiconductor Detectors and Materials
- Perovskite Materials and Applications
- Semiconductor materials and devices
- Electronic and Structural Properties of Oxides
- Advanced Electron Microscopy Techniques and Applications
- Semiconductor materials and interfaces
- Advanced Thermoelectric Materials and Devices
- Advanced Battery Technologies Research
- Thermal properties of materials
- Advanced X-ray and CT Imaging
- Copper-based nanomaterials and applications
- Electrocatalysts for Energy Conversion
- Transition Metal Oxide Nanomaterials
- Graphene research and applications
- Metal Extraction and Bioleaching
- Fuel Cells and Related Materials
- 2D Materials and Applications
Argonne National Laboratory
2016-2025
Northwestern University
2018-2025
Arizona State University
2022-2024
Center for Nanoscale Materials
2011-2024
First Solar (United States)
2022-2023
Purdue University West Lafayette
2022-2023
University of Chicago
2015-2023
Massachusetts Institute of Technology
2005-2021
Lawrence Berkeley National Laboratory
2021
Oak Ridge National Laboratory
2021
Combine and conquer Platinum (Pt)–group metals, which are scarce expensive, used for the demanding oxygen reduction reaction (ORR) in hydrogen fuel cells. One competing approach reducing their use is to create nanoparticles with earth-abundant metals increase activity surface area; another replace them such as cobalt (Co) carbide or nitride sites. Chong et al. thermally activated a Co metal-organic framework compound ORR-active sites then grew PtCo alloy on this substrate. The resulting...
Layered molybdenum disulfide has demonstrated great promise as a low-cost alternative to platinum-based catalysts for electrochemical hydrogen production from water. Research effort on this material focused mainly synthesizing highly nanostructured that allows the exposure of large fraction active edge sites. Here we report promising microwave-assisted strategy synthesis narrow nanosheets with edge-terminated structure and significantly expanded interlayer spacing, which exhibit striking...
An efficient method for the prediction of fundamental band gaps in solids using density functional theory (DFT) is proposed. Generalizing Delta self-consistent-field (ΔSCF) to infinite solids, Δ-sol based on total-energy differences and derived from dielectric screening properties electrons. Using local semilocal exchange-correlation functionals (local generalized gradient approximations), we demonstrate a 70% reduction mean absolute errors compared Kohn-Sham over 100 compounds with...
We compare the accuracy of conventional semilocal density functional theory (DFT), $\text{DFT}+U$ method, and Heyd-Scuseria-Ernzerhof (HSE06) hybrid for structural parameters, redox reaction energies, formation energies transition metal compounds. Conventional DFT functionals significantly underestimate potentials these Zhou et al. [Phys. Rev. B 70, 235121 (2004)] addressed this issue with a linear-response scheme calculating $U$ values. show that Li intercalation prominent Li-ion battery...
Silicon is of significant interest as a next-generation anode material for lithium-ion batteries due to its extremely high capacity. The reaction lithium with crystalline silicon known present rich range phenomena, including electrochemical solid state amorphization, crystallization at full lithiation Li15Si4 phase, hysteresis in the first lithiation–delithiation cycle, and highly anisotropic samples. Very little about these processes an atomistic level, however. To provide fundamental...
The formation mechanism and composition of the solid electrolyte interphase (SEI) in lithium ion batteries has been widely explored. However, relatively little is known about function SEI as a transport medium. Such critical information directly relevant to battery rate performance, power loss, capacity fading. To partially bridge this gap case inorganic compounds, we report herein results first-principles calculations on defect thermodynamics, dominant diffusion carriers, pathways...
Synthetic two-dimensional polymorphs of boron, or borophene, have attracted attention because their anisotropic metallicity, correlated-electron phenomena, and diverse superlattice structures. Although borophene heterostructures been realized, ordered chemical modification has not yet reported. Here, we synthesize "borophane" by hydrogenating with atomic hydrogen in ultrahigh vacuum. Through atomic-scale imaging, spectroscopy, first-principles calculations, the most prevalent borophane...
A lack of consensus persists regarding the origin photoluminescence in silicon nanocrystals. Here we report pressure-dependences X-ray diffraction and from alkane-terminated colloidal particles. We determine diamond-phase bulk modulus, observe multiple phase transitions, importantly find a systematic red shift that matches X(conduction)-to-Γ(valence) transition crystalline silicon. These results, reinforced by calculations, suggest efficient photoluminescence, frequently attributed to...
Despite the many advantages (e.g., suitable band gap, exceptional optical absorptivity, earth abundance) of pyrite as a photovoltaic material, its low open-circuit voltage (OCV) has remained biggest challenge preventing use in practical devices. Two most widely accepted reasons for cause OCV are (i) Fermi level pinning due to intrinsic surface states that appear gap states, and (ii) presence metastable polymorph, marcasite. In this paper, we investigate these claims, via density-functional...
Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels predict experimental condensed phase properties (i.e., density heat vaporization). The performance this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials 1250 cluster interaction...
Electrochemical potential/pH (Pourbaix) diagrams underpin many aqueous electrochemical processes and are central to the identification of stable phases metals for ranging from electrocatalysis corrosion. Even though standard DFT calculations potentially powerful tools prediction such diagrams, inherent errors in description transition metal (hydroxy)oxides, together with neglect van der Waals interactions, have limited reliability predictions even simplest pure bulk compounds, corresponding...
To evaluate the possible utility of single layer graphene for applications in Li ion batteries, an extensive series periodic density functional theory (DFT) calculations are performed on sheets with both point and extended defects a wide range lithium coverages. Consistent recent reports, it is found that adsorption defect-free not thermodynamically favorable compared to bulk metallic Li. However, surfaces activated by generally bind more strongly, interaction strength sensitive nature their...
The ability to predict a semiconductor's band edge positions in solution is important for the design of water-splitting photocatalyst materials. In this paper, we introduce first-principles method compute conduction-band minima semiconductors relative water ${\mathrm{H}}_{2}$O/H${}_{2}$ level using density functional theory with semilocal functionals and classical molecular dynamics. We test on six well known materials: TiO${}_{2}$, WO${}_{3}$, CdS, ZnSe, GaAs, GaP. predicted are within 0.34...
Abstract It has become clear that cycling lithium‐oxygen cells in carbonate electrolytes is impractical, as electrolyte decomposition, triggered by oxygen reduction products, dominates the cell chemistry. This research shows employing an α ‐MnO 2 /ramsdellite‐MnO electrode/electrocatalyst results formation of lithium‐oxide‐like discharge products propylene carbonate, which been reported to be extremely susceptible decomposition. X‐ray photoelectron data have shown what are likely lithium...
The ever-increasing power of modern supercomputers, along with the availability highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling materials. In particular, molecular dynamics (MD) led breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration MD simulations X-ray characterization demonstrated promise real-time 3-D materials on atomic scale. popularity is driven by...
We compute the transient dynamics of phonons in contact with high energy "hot" charge carriers 12 polar and nonpolar semiconductors, using a first-principles Boltzmann transport framework. For most materials, we find that decay electronic temperature departs significantly from single-exponential model at times ranging 1 to 15 ps after excitation, phenomenon concomitant appearance nonthermal vibrational modes. demonstrate these effects result slow thermalization within phonon subsystem,...
We introduce a bond order potential (BOP) for stanene based on an ab initio derived training data set. The is optimized to accurately describe the energetics, as well thermal and mechanical properties of free-standing sheet, used study diverse nanostructures stanene, including tubes ribbons. As representative case study, using potential, we perform molecular dynamics simulations stanene's structure temperature-dependent conductivity. find that highly rippled, far in excess other 2-D...
Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural dynamical properties with high precision. These play critical roles in understanding a wide variety of systems, from catalysis to polymers, nanomaterials macromolecules, energy quantum materials. In recent years, neutron have received significant boost due the development increased application machine learning problems. This article reviews progress applying...
High-throughput screening based on a DFT+ML framework enables identification of novel halide perovskite alloys for solar cell absorption.
Abstract New materials for electrochemical energy storage and conversion are the key to electrification sustainable development of our modern societies. Molecular modelling based on principles quantum mechanics statistical as well empowered by machine learning techniques can help us understand, control design at atomistic precision. Therefore, this roadmap, which is a collection authoritative opinions, serves gateway both experts beginners have quick overview current status corresponding...