Area of research
Materials Chemistry · Radiation
Research interest
Research interests include Materials science, Computer science, Graphene, Scalability, Interatomic potential, and Thermal conductivity.
Development and assessment of hierarchical multi-reward reinforcement learning based potential for silicene with state-of-the-art models
Ab Initio-Based Bond Order Potential for Arsenene Polymorphs Developed via Hierarchical Reinforcement Learning
Learning in continuous action space for developing high dimensional potential energy models
Atomic Cross-Talk at the Interface: Enhanced Lubricity and Wear and Corrosion Resistance in Sub 2 nm Hybrid Overcoats via Strengthened Interface Chemistry
Graphene overcoats for ultra-high storage density magnetic media
Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data
Machine learning a bond order potential model to study thermal transport in WSe<sub>2</sub>nanostructures
Slippery and Wear-Resistant Surfaces Enabled by Interface Engineered Graphene
Machine Learning Applied to a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures
Teaching an Old Dog New Tricks: Machine Learning an Improved TIP3P Potential Model for Liquid–Vapor Phase Phenomena
Silicon compatible Sn-based resistive switching memory
Evolutionary Optimization of a Charge Transfer Ionic Potential Model for Ta/Ta-Oxide Heterointerfaces
<i>Ab Initio</i>-Based Bond Order Potential to Investigate Low Thermal Conductivity of Stanene Nanostructures