A5066701381- Machine Learning in Materials Science
- Solidification and crystal growth phenomena
- Electronic Packaging and Soldering Technologies
- Aluminum Alloy Microstructure Properties
- High Temperature Alloys and Creep
- 3D IC and TSV technologies
- Mineral Processing and Grinding
- Intermetallics and Advanced Alloy Properties
- X-ray Diffraction in Crystallography
- Advanced Welding Techniques Analysis
- Manufacturing Process and Optimization
- Industrial Vision Systems and Defect Detection
- Thermal properties of materials
- Nuclear Materials and Properties
- Metal and Thin Film Mechanics
- Additive Manufacturing Materials and Processes
- Crystallization and Solubility Studies
- Aluminum Alloys Composites Properties
- nanoparticles nucleation surface interactions
- Probabilistic and Robust Engineering Design
- Magnetic Properties and Applications
- Advanced materials and composites
- High Entropy Alloys Studies
- Fusion materials and technologies
- Additive Manufacturing and 3D Printing Technologies
Texas A&M University
2016-2024
Mitchell Institute
2018-2023
College Station Medical Center
2018-2019
Galvanostatic electrodeposition from Grignard reagents in symmetric Mg–Mg cells is used to map Mg morphologies fractal aggregates of 2D nanoplatelets highly anisotropic dendrites with singular growth fronts and entangled nanowire mats.
Additive manufacturing (AM), especially Laser Powder-Bed Fusion (L-PBF), provides alloys with unique properties, but faces printability challenges like porosity and cracks. To address these issues, a co-design strategy integrates chemistry process indicators to efficiently screen the design space for defect-free combinations. Physics-based models visualization tools explore space, KGT guide microstructural design. The approach combines experiments, databases, deep learning models, Bayesian...
In this study, we propose a novel microstructure-sensitive Bayesian optimization (BO) framework designed to enhance the efficiency of materials discovery by explicitly incorporating microstructural information. Traditional design approaches often focus exclusively on direct chemistry-process-property relationships, overlooking critical role microstructures. To address limitation, our integrates descriptors as latent variables, enabling construction comprehensive process-structure-property...
Strain-induced suppression of the miscibility gap in solid solutions Mg<sub>2</sub>Si and Mg<sub>2</sub>Sn was studied to reduce lattice thermal conductivity.
Abstract Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design materials. In present work, we propose non-intrusive materials informatics high-throughput exploration and analysis a synthetic microstructure space using machine learning-reinforced multi-phase-field modeling scheme. We specifically study interface energy as one most uncertain inputs in phase-field its impact on shape contact...
We studied the evolution of Cu6Sn5 and Cu3Sn intermetallic compounds under large thermal gradients for assisted directional solidification in Cu/Sn/Cu micro solder units fast bonding 3-D IC packaging. Thermomigrationenhanced growth dissolution phasesare observed tracked using a CALPHAD-reinforced multiphase-field model that accounts thermodynamics kinetics Cu(Sn) reacting system. The results show process follows reaction-controlled regime (n~1) during gradient bonding, proceeding faster than...
During the laser powder bed fusion (L-PBF) process, built part undergoes multiple rapid heating-cooling cycles, leading to complex micro structures with nonuniform properties. In present work, a computational framework, which couples finite element thermal model non-equilibrium phase field was developed investigate solidification structure of Ni-Nb alloy during L-PBF. The framework is utilized predict spatial variation morphology and size as well segregation in single-track melt pool...