Dongwon Shin Senior R&D Staff Member Contact SHIND@ORNL.GOV All Publications Effect of Sn microalloying on the nucleation of L12 Al3Zr precipitates in a dilute aluminum-zirconium alloy The many faces of θ'-Al2Cu precipitates: Energetics of pristine and solute segregated Al/θ' semi-coherent interfaces Effect of microalloying additions on microstructural evolution and thermal stability in cast Al-Ni alloys Low-Temperature Nucleation Behavior of L12 Al3Zr Precipitates in a Dilute Al-Zr-Sn Alloy Effects of Sn addition on precipitation of Al–Zn–Mg alloy at early stage of natural aging Application of machine learning to sporadic experimental data for understanding epitaxial strain relaxation First-principles study of Al/Al3Ni interfaces A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions HPC Analytics of Fused Thermal Plants Data to Optimize Operating Envelope A machine learning approach to predict thermal expansion of complex oxides Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys DATA FUSION: A PROJECT UPDATE & PATHWAY FORWARD Atomic structures of interfacial solute gateways to θ′ precipitates in Al-Cu alloys Computational thermodynamic study of SiC chemical vapor deposition from MTS‐H2 Aging behavior and strengthening mechanisms of coarsening resistant metastable θ' precipitates in an Al–Cu alloy Uncertainty Quantification of Machine Learning Predicted Creep Property of Alumina-Forming Austenitic Alloys Solute-vacancy clustering in aluminum Coupling physics in machine learning to predict properties of high-temperatures alloys The synergistic role of Mn and Zr/Ti in producing θ′/L12 co-precipitates in Al-Cu alloys Equilibrium solute segregation to matrix-θ′ precipitate interfaces in Al-Cu alloys from first principles Primary solidification of ternary compounds in Al-rich Al–Ce–Mn alloys Advanced data science toolkit for non-data scientists – A user guide ASCENDS: Advanced data SCiENce toolkit for Non-Data Scientists Data analytics approach for melt-pool geometries in metal additive manufacturing Pagination Current page 1 Page 2 Page 3 Next page ›› Last page Last » Key Links Google Scholar ORCID Advanced data SCiEnce toolkit for Non-Data scientists Organizations Physical Sciences Directorate Materials Science and Technology Division Materials Theory, Modeling and Simulation Section Microstructural Evolution Modeling Group
Research Highlight Deep Learning for Fast and Accurate Predictions of Crystallographic and Thermodynamic Properties of Multi-Component Solid Solution Alloys
Research Highlight Deep learning for fast and accurate predictions of crystallographic and thermodynamic properties of multi-component solid solution alloys
