Research Interests & Experience

My Undergraduate Thesis Presentation

Can gradient wettability enhance heat transfer in nanoscale? Our Undergraduate Thesis defense.

Molecular dynamics (MD) is a computer simulation method for analyzing the physical movements of atoms and molecules. The atoms and molecules are allowed to interact for a fixed period of time, giving a view of the dynamic "evolution" of the system. In the most common version, the trajectories of atoms and molecules are determined by numerically solving Newton's equations of motion for a system of interacting particles, where forces between the particles and their potential energies are often calculated using interatomic potentials or molecular mechanics force fields. The method is applied mostly in chemical physics, materials science, and biophysics.

I was introduced to MD simulations in 2017. Since then I have been part of a number of research projects, where my job was to conduct MD simulations in the open-source MD simulation software LAMMPS. I have developed/co-developed a number of LAMMPS input codes such as:

1. Uni-directional and bi-directional tensile test simulation.

2. Tensile test simulation of nanowires.

3. Vibration test simulation of nanowires.

4. Explosive boiling simulation of liquid argon and water.

5. Simulation to calculate viscosity of nanofluids.

6. Unidirectional solidification simulation of metals.

7. Combustion simulation of methane using ReaxFF potential.

I have published 3 MD related publications (details on publications section). Currently I am learning to develop Modified Embedded Atom Method (MEAM) potential for AlSi12 additive manufacturing alloy. I intend to be an expert in MD simulation and incorporate MD with data science and experimental research.

Functionally graded materials (FGM) are considered one of the most promising materials among the class of advanced fabricated materials. They consist of two or more different materials, where the composition continuously varies along a dimension following a particular function. FGMs are conceived as a solution to solve high-stress concentration, high-temperature creep and material delamination challenges that are common in other fabricated materials such as composites. These enhanced thermal and mechanical properties render FGM a suitable candidate for manufacturing structures of aeroplane, automobile engine components and protective coatings for turbine blades. Due to the applications in the fields of aerospace, automobile, medicine and energy, the research efforts to characterize the mechanical and thermal properties of FGMs have increased rapidly in recent years.

My work on functionally graded Cu-Ni nanowire is the first molecular dynamics study on functionally graded materials. In this study, I have investigated the modulating effects of the grading function on elastic and vibrational properties. Also I have compared the MD results with various micromechanical models. The work has been published in Elsevier's Composites Part B: Engineering (IF: 9.078). Furthermore in this work, I co-developed a NanoHub tool to generate functionally graded structures for LAMMPS. The tool has 118 users across 6 continents.

The potential of FGMs in nanoscale applications really fascinates me. I intend to conduct in-depth study of FGMs in nanoscale using data science.

In recent years, we have witnessed several pioneering advancements in the fields of machine learning (ML) and neural network (NN). Typically, a neural network can be characterized as a series of algorithms that can recognize the underlying relationships between a set of data accurately through a process that mimics the way the human brain operates. The accuracy of the NN depends especially on the accuracy of the available training data. Training data are extracted from physically realistic models of a system or process with different degrees of complexity. The idea of coupling computational physics and NN is fairly new and bears great promise.

Since my university does not offer a course in machine learning, I learnt the basics of machine learning through Coursera in 2019. Also Satyajit Mojumder of Northwestern University, USA provided me reading materials on deep neural network. Under his supervision, I undertook a project that uses deep multi-fidelity physics informed neural network to accelerate molecular dynamics simulations' predictive capability. The work has been published in Computational Material Science (Elsevier). All the codes regarding neural networks in this work have been written by me. Feel free to check the paper and the codes.

Currently I am working on a project related to mechanistic data science with Professor Wing Kam Liu, Northwestern University. I hope to be a computational material scientist who is proficient in data science and deep learning.

Rapid progress in the synthesis and processing of materials with structure on nanometer length scales has created a demand for greater scientific understanding of thermal transport in nanoscale devices, individual nanostructures, and nanostructured materials. Both Finite Element Method and Molecular dynamics can significantly help us study the physical phenomenon of nanoscale thermal transport.

I have published 3 papers on convective heat transfer in nanochannels and cavities. In these studies, I have used COMSOL multiphysics to simulate different heat transfer configurations. I have also worked on thin film phase transition phenomena using molecular dynamics study. My B.Sc thesis is based on phase transition of thin film liquid argon over functionally gradient surface. I want to understand nanoscale thermal transport to extract its application in novel material processing.