▶ Development and application of Neural Network interatomic potentials for a variety of materials including nuclear energy materials, battery materials, advanced alloys, nano-particle-ligand systems, and neuromorphic computing materials. ▶ Application of machine learning/deep learning in materials modeling such as constitutive relations.▶ Atomistic simulation/modeling on liquid/solid interfaces in nuclear energy applications and batteries. ▶ Large-scale (length-/time-scale) application of machine learning interatomic potentials in atomic simulations.

▶ Atomistic modeling for materials under complex and extreme conditions.▶ Milestone work on fundamental understanding of chemical short-range order in high-/medium-entropy alloys (Nature Communications, 10, 3563, 2019, Google Scholar citation > 300). ▶ Fundamental work on high-speed dislocation physics underlying some strongly correlated plasticity (Physical Review Letters, 117, 165501, 2016; Acta Materialia 119, 229, 2016). ▶ Development of highly efficient saddle point calculation method for systems with > 10^7 atoms (Acta Materialia 145, 19, 2018).▶ Insights into nano-materials deformation initiated by dislocation nucleation (Acta Materialia, 145, 19, 2018).▶ New mechanisms for damage initiation and solid solution hardening due to oxygen impurity in BCC metals such as Nb (Acta Materialia, 168, 331, 2019; Acta Materialia, 179, 107, 2019).