A5053956015- Intermetallics and Advanced Alloy Properties
- Machine Learning in Materials Science
- 2D Materials and Applications
- MXene and MAX Phase Materials
- Rare-earth and actinide compounds
- X-ray Diffraction in Crystallography
- Quasicrystal Structures and Properties
- Aluminum Alloys Composites Properties
- Aluminum Alloy Microstructure Properties
- Semiconductor materials and interfaces
- Corrosion Behavior and Inhibition
- Graphene research and applications
- Electronic Packaging and Soldering Technologies
- Perovskite Materials and Applications
- Advanced Materials Characterization Techniques
- Crystallography and molecular interactions
- Chemical Thermodynamics and Molecular Structure
- Boron and Carbon Nanomaterials Research
- Glass properties and applications
- Solid-state spectroscopy and crystallography
- Material Dynamics and Properties
- Luminescence Properties of Advanced Materials
- Metallurgical Processes and Thermodynamics
- Microstructure and mechanical properties
- Advanced materials and composites
University at Buffalo, State University of New York
2024
University of Florida
2017-2022
Los Alamos National Laboratory
2018
Coe College
2007
The discovery of two-dimensional (2D) materials comes at a time when computational methods are mature and can predict novel 2D materials, characterize their properties, guide the design for applications. This article reviews recent progress in approaches research. We discuss techniques provide an overview ongoing research field. begin with known common methods, available cyber infrastructures. then move onto discussing stability criteria structure prediction, interactions monolayers...
Abstract Large-density functional theory (DFT) databases are a treasure trove of energies, forces, and stresses that can be used to train machine-learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database moment tensor (MTPs) four carbide systems: CHfTa, CHfZr, CMoW, CTaTi. The resulting MTPs relax ~6300 random symmetric structures, subsequently improved via active learning generate robust (RP) wide variety accurate (AP) designed...
Phase diagram and diffusion coefficients of the Fe-Zn binary system are required to predict control microstructure galvanized zinc coatings thus were systematically investigated in temperature range from 700 1100 °C using nine novel Fe/Zn liquid-solid couples (LSDCs). The equilibrium compositions α Γ phases determined agree well with recently established phase diagram. extracted interdiffusion α-Fe at temperatures between 750 forward-simulation analysis (FSA) extend experimental...
Two-dimensional semiconductor phosphorene has attracted extensive research interests for potential applications in optoelectronics, spintronics, catalysis, sensors, and energy conversion. To harness phosphorene's requires a better understanding of how intrinsic defects control carrier concentration, character, mobility. Using density functional theory charge correction scheme to account the appropriate boundary conditions, we conduct comprehensive study effect structure on formation energy,...
Large density functional theory databases are a treasure trove of energies, forces and stresses that can be used to train machine learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database moment tensor four carbide systems. HfTaC, HfZrC, MoWC TaTiC. The resulting MTPs relax 6300 random symmetric structures, subsequently improved via active learning generate robust applied wide variety accurate designed only low-energy This...
<title>Abstract</title> Large density functional theory (DFT) databases are a treasure trove of energies, forces and stresses that can be used to train machine learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database moment tensor (MTPs) four carbide systems: HfTaC, HfZrC, MoWC TaTiC. The resulting MTPs relax ∼6300 random symmetric structures, subsequently improved via active learning generate robust (RP) applied wide variety...
Automotive applications need low-cost, lightweight, high-temperature alloys to increase vehicle efficiency. The Al–Fe–Si system provides an opportunity develop such a material, as it consists of three low-cost elements that are all abundant in nature. Specifically, the τ11-Al4Fe1.7Si ternary intermetallic phase is high-temperature, lightweight with high strength and good corrosion resistance. However, this exhibits narrow compositional range stability, resulting undesirable microstructures...
The two-dimensional semiconductor phosphorene has attracted extensive research interests for potential applications in optoelectronics, spintronics, catalysis, sensors, and energy conversion. To harness phosphorene's requires a better understanding of how intrinsic defects control carrier concentration, character, mobility. Using density-functional theory charge correction scheme to account the appropriate boundary conditions, we conduct comprehensive study effect structure on formation...
The intermetallic $\tau_{11}$ Al$_4$Fe$_{1.7}$Si phase is of interest for high-temperature structural application due to its combination low density and high strength. We determine the crystal structure through a powder neutron diffraction functional theory calculations. Using Pawley Rietveld refinements data provides an initial model. Since Al Si have nearly identical scattering lengths, we use density-functional calculations their preferred site occupations. exhibits hexagonal with space...