Computational Materials Theory Group29
Calculation is increasingly replacing experimentation in design of useful functional materials. We use quantum mechanics and atomistic simulations for understanding and control of matter, energy, and information at the microscopic scales of materials science. When combined with machine learning, first-principles materials theory potentially leads to new opportunities for creativity in materials physics.
- Materials by design
- Machine learning
- Physics outside physics
Advisor Professor : Kang, Joongoo
Computational Materials Theory Group Homepage
- Materials by design
- Machine learning
- Physics outside physics
Advisor Professor : Kang, Joongoo
Computational Materials Theory Group Homepage
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Recent Submissions
RSS 1.0
RSS 2.0
ATOM 1.0
- Unveiling the Nanocluster Conversion Pathway for Highly Monodisperse InAs Colloidal Quantum Dots
- Identifying and clearing individual oxygen impurities on graphene through the use of NO2 as a radical scavenger
- First-principles Study of Non-Isovalent Si2AlP And Si2ZnS Alloys Covalent, Ionic and Their Mixed Phases
- Synthesis of alpha-Borylmethyl-(E)-allylborons via Cu-Catalyzed Diboration of 1-Substituted Allenols and Their Application in Stereoselective Aldehyde Allylation
- Enhanced Reactivity of Magic-Sized Inorganic Clusters by Engineering the Surface Ligand Networks