Area of research
Materials Chemistry · Atmospheric Science
Research interest
Research interests include Machine Learning in Materials Science, nanoparticles nucleation surface interactions, Computational Drug Discovery Methods, and Advanced Memory and Neural Computing.
Autonomous platform for solution processing of electronic polymers
Development and assessment of hierarchical multi-reward reinforcement learning based potential for silicene with state-of-the-art models
Artificial Intelligence for Conjugated Polymers
Depolymerizable and recyclable luminescent polymers with high light-emitting efficiencies
Machine Learning a Simple Interpretable Short-Range Potential for Silica
Ab Initio-Based Bond Order Potential for Arsenene Polymorphs Developed via Hierarchical Reinforcement Learning
Self-Driving Laboratory for Polymer Electronics
Understanding and control of Zener pinning via phase field and ensemble learning
CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment
A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery
Simulation methods for self-assembling nanoparticles
Multi-reward reinforcement learning based development of inter-atomic potential models for silica
Surface premelting of ice far below the triple point
Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images
Machine learning overcomes human bias in the discovery of self-assembling peptides
Learning in continuous action space for developing high dimensional potential energy models
Structure of Tetrahymena telomerase-bound CST with polymerase α-primase
Accurate determination of solvation free energies of neutral organic compounds from first principles
Machine learning the metastable phase diagram of covalently bonded carbon
Multi-reward Reinforcement Learning Based Bond-Order Potential to Study Strain-Assisted Phase Transitions in Phosphorene
Rapid 3D nanoscale coherent imaging via physics-aware deep learning
Artificial Intelligence-Guided <i>De Novo</i> Molecular Design Targeting COVID-19
Nanoporous Dielectric Resistive Memories Using Sequential Infiltration Synthesis
Machine learning enabled autonomous microstructural characterization in 3D samples
Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies
Creation of Single-Photon Emitters in WSe<sub>2</sub> Monolayers Using Nanometer-Sized Gold Tips
Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data
Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data
Active learning a coarse-grained neural network model for bulk water from sparse training data
Machine learning coarse grained models for water