A5082049643- Material Dynamics and Properties
- Machine Learning in Materials Science
- Glass properties and applications
- Phase-change materials and chalcogenides
- Microstructure and mechanical properties
- X-ray Diffraction in Crystallography
- Crystallography and molecular interactions
- Hydrogen Storage and Materials
- Solid-state spectroscopy and crystallography
- Diamond and Carbon-based Materials Research
- Energy Load and Power Forecasting
- Hydrogen embrittlement and corrosion behaviors in metals
- High Entropy Alloys Studies
- Metal and Thin Film Mechanics
- Crystallization and Solubility Studies
- Thin-Film Transistor Technologies
- Power Transformer Diagnostics and Insulation
- Nuclear Physics and Applications
- High-pressure geophysics and materials
- Carbon Nanotubes in Composites
- Silicon and Solar Cell Technologies
- Organic and Molecular Conductors Research
- Metallic Glasses and Amorphous Alloys
- Gas Sensing Nanomaterials and Sensors
- Electron Spin Resonance Studies
Los Alamos National Laboratory
2020-2024
Oak Ridge National Laboratory
2018-2020
Ohio University
2014-2017
Spin-phonon coupling plays an important role in single-molecule magnets and molecular qubits. However, there have been few detailed studies of its nature. Here, we show for the first time distinct couplings g phonons CoII(acac)2(H2O)2 (acac = acetylacetonate) deuterated analogs with zero-field-split, excited magnetic/spin levels (Kramers doublet (KD)) S 3/2 electronic ground state. The are observed as avoided crossings magnetic-field-dependent Raman spectra constants 1-2 cm-1. Far-IR reveal...
Abstract The general and practical inversion of diffraction data–producing a computer model correctly representing the material explored–is an important unsolved problem for disordered materials. Such modeling should proceed by using our full knowledge base, both from experiment theory. In this paper, we describe robust method to jointly exploit power ab initio atomistic simulation along with information carried data. is applied two very different systems: amorphous silicon compositions...
Hydrogen-containing materials are of fundamental as well technological interest. An outstanding question for both is the amount hydrogen that can be incorporated in such materials, because determines dramatically their physical properties electronic and crystalline structure. The number atoms a metal controlled by interaction hydrogens with hydrogen-hydrogen interactions. It established minimal possible distances conventional hydrides around 2.1 Å under ambient conditions, although closer...
We apply a method called ``force-enhanced atomic refinement'' (FEAR) to create computer model of amorphous silicon $(a\text{-Si})$ based upon the highly precise x-ray diffraction experiments Laaziri et al. [Phys. Rev. Lett. 82, 3460 (1999)]. The logic underlying our calculation is estimate structure real sample $a\text{-Si}$ using experimental data and chemical information included in nonbiased way, starting from random coordinates. close agreement with experiment also sits at suitable...
Hydrogenated amorphous silicon is a technologically important material, vital to applications as varied photovoltaics, thin-film transistors, and ``night vision'' goggles. All of these rely on the function dopants, but, remarkably, our understanding how most common dopants infiltrate into material perform crucial electronic functions remains largely empirical. The authors provide comprehensive ab initio study two key in silicon, including lattice dynamics hydrogen hopping passivation. This...
The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to autonomously build while remaining operational. Through a series query strategies, the machine can evaluate whether newly encountered data fall outside scope existing training set. In this study, we introduce PowerModel-AI, an end-to-end software designed accurately predict AC power...
We present a novel machine learning based surrogate modeling method for predicting spatially resolved 3D microstructure evolution of polycrystalline materials under uniaxial tensile loading. Our approach is orders magnitude faster than the existing crystal plasticity methods enabling simulation large volumes that would be otherwise computationally prohibitive. This work major step beyond ML-based results, which have been limited to either 2D structures or only providing average, rather...
Stability and response of supramolecular forms is important to many areas in materials science, contributions from vibrations can be crucial. We have collected the first spectra organic molecular crystals polymorphic cocrystals using next-generation, high-signal VISION spectrometer far-infrared (FIR) mid-infrared (MIR) range. Unambiguously different spectral signatures were found for carbamazepine two polymorphs carbamazepine-saccharin cocrystal, including numerous modes undetectable with...
The slow microstructural evolution of materials often plays a key role in determining material properties. When the unit steps process are slow, direct simulation approaches such as molecular dynamics become prohibitive and Kinetic Monte-Carlo (kMC) algorithms, where state-to-state system is represented terms continuous-time Markov chain, instead frequently relied upon to efficiently predict long-time evolution. accuracy kMC simulations however relies on complete accurate knowledge reaction...
Pressure-induced effects in alkali hydrides are investigated using a plane-wave density functional theory method. For the first time, we have measured inelastic neutrons scattering (INS) spectra of NaH at pressures 1 and 2 GPa used it to validate INS simulated from first-principles calculations both local approximation (LDA) generalized gradient (GGA). We found that LDA describes lattice dynamics better compared GGA. Thermodynamic properties such as parameters, bulk modulus, their...
The general and practical inversion of diffraction data-producing a computer model correctly representing the material explored - is an important unsolved problem for disordered materials. Such modeling should proceed by using our full knowledge base, both from experiment theory. In this paper, we describe robust method to jointly exploit power ab initio atomistic simulation along with information carried data. applied two very different systems: amorphous silicon compositions solid...