Modeling and Simulaiton
Computation and Simulation: Traditional Development and AI-Powered Tools
(I) development of polarizable force field
(II) Transferable machine learning potential for simulaions
(III) constant electric potential method
Artificial Intelligence for Science
Transferable Artificial Intelligence 4 Science
(I) Graph-reinforcedand physics-learned machine learning
(II) Generative AI for discovery of materials, chemistry and reactions
(III) permutable graph nueral netowrk for reactions, interfaces and structural assembly
Materials Discovery
Discovery of Materials for Emerging Applications
(I) Energy and Electrochemistry
a. Multi-valent batteries (Mg-ion, Zn-ion, Fe-ion)
b. synergistic solubility and novel electrolytes
(II) Interfaces and interphases
(III) Charge transfer
Phase-transformation
Solid-solid phase transformations have emerged as fascinating domains, often leading to unexpected material behaviors and unlocking a plethora of novel applications. These transformations, and the associated mechanistic insights, lie at the heart of some of the most groundbreaking research topics in modern material science, including semiconductors, neuromorphic devices, energy-absorbing material for energy storage and structural materials.
Separations
(I) removal of organic contaminants and Per-/poly-fluoroalkyl substances (PFAS)
(II) inhibiting in-organic scales