(7cz) Targeted Design of Next-Generation Materials

With understanding comes creativity, inspiration, and imagination; this sentence highlights the roadmap of my research group.

Research Interests:

Advancement of nano and biotechnology in the past decade have revealed exciting opportunities to design new classes of materials that could potentially revolutionize several industrial sectors in the near future. Unfortunately, we haven’t exploited those opportunities yet because we still don’t fully understand the working mechanisms of these new materials. For example, thermotropic liquid crystals (LCs) have emerged to potentially replace traditional biological sensors because they are label-free and inexpensive. These materials are also being developed as drug-delivery systems. Despite their tremendous potentials, the molecular mechanism of biosensing and the mechanics of cargo delivery remains elusive and limited to experimental trial-and-errors. Another example pertains to the controlled mineralization of inorganic compounds with hierarchical structures such as calcium carbonate, silica, and calcium phosphate. These materials have extensively been used in numerous applications, and their functionality highly depends on the morphology of very small building blocks. Despite their fundamental and commercial importance, the structure of those small building-blocks and a clear pathway that connects the primary solution of the ions to the nanometer-sized clusters, and to the final crystals is still missing. The classical nucleation and growth theory fails to explain the formation of amorphous nanoparticles; and a widely acceptable theory hasn’t been suggested yet. Bioengineered metallic nanoparticles present yet another class of programmable materials with broad applications ranging from catalysis to therapeutics, and biomarkers. These nanoparticles are super-small (< 5nm), they are synthesized in a complex environment (ligands, reducing agents, water), and their functionality is mainly controlled by their surface structures. Experiments such as transmission electron microscopy (TEM) and Synchrotron X-ray techniques provide invaluable details about the overall structures of these nanoparticles but a realistic picture of the atomic-level surface structures and the mechanism of structural changes during catalytic reactions is absent. In the past, we have manifested a distribution of sizes and atomic structures into a single particle but we need to move beyond that. Additionally, we still don’t understand how the surface structure of the particles evolve during catalytic cycles. In my research group, we’ll provide answers to all of these questions using modeling and simulations in conjunction with collaborative experiments. The following paragraphs elaborate on my plan of attack to address these problems.

To understand the working mechanism of stimuli-responsive LC-based drug delivery systems, one must understand i) the nano-scale details and energetics related to the molecular organizations and diffusions near the interfaces, and ii) the micro-scale phenomenological behavior of the entire vesicles due to diffusion of the molecules in combination with elasticity and reorientation of the LC phase. In my group, we’ll develop necessary computational toolboxes to gain those understandings. First, we’ll perform large-scale all-atom molecular dynamics (MD) simulations of the interfaces to resolve orientational features of the molecules, compute energetics of the molecular diffusions, and provide input for phenomenological modeling. Next, we’ll design a massively parallel computational framework to perform continuum level simulations of the real-size vesicles. Our computational framework will combine the effect of an external stimuli (addition of electrolytes, applying electric or magnetic fields, etc) with i) three-dimensional (3D) diffusion of the molecules and ii) elasticity and reorientation of the liquid crystalline phases. Our all-atom simulations and experimental data will inform our continuum level models unlike previous studies where the simulations parameters were chosen with little or no physical insights. We will develop our computational framework using CUDA platform and MPI protocol to make use of inexpensive yet powerful graphical processing units (GPUs) and parallel nodes.

To understand the inner working mechanism of the LC-based sensors, we’ll simulate a real biological sensor using large-scale all-atom MD simulations. Particularly, we’ll focus on self-assembly of shaped noble metal nanoparticles such as gold and platinum as well as amphiphilic molecules at the interfaces and defective regions. To this end, we’ll develop a new polarizable model of the metallic phase that is compatible with the existing models of the LC and other organic phases. Here, our goal is to answer long-lasting fundamental questions related to self-assembly at the interfaces and defective regions. Our detailed simulations will also guide continuum-level calculations. The outcome of our studies will eventually facilitate de novo design of next-generation biosensors.

To control the mineralization process of inorganic multivalent ions, one must first understand the early-stage nucleation and growth pathways. Such understanding doesn’t exist yet because i) the sub-3 nm early clusters are inaccessible to most structural characterization techniques, and ii) pre-nucleation process happens very fast (non-traditional). Fortunately, atomistic simulations can provide the perfect time-resolution and the length-resolution to resolve those difficulties. Because these systems are highly charged (high concentration of multivalent ions in water), polarizability of the ions may not be neglected. Since the process of nucleation and growth involves ions in fully hydrated state as well as fully dehydrated phases (clusters and crystals), the computational models must reproduce both solution and crystal properties simultaneously. Such models do not exist yet but we will develop polarizable models of carbonates and silica in my group to study the nucleation pathways of these minerals. We’ll then develop a general phenomenological theory to explain the nucleation and growth pathways of these systems and other similar materials.

To resolve the full distribution of the atomic-level structures of palladium nanocatalysts, we’ll develop a novel toolbox to drive a hybrid restrained-ensemble MC/MD simulations in accordance with experiments. Particularly, we’ll make use of i) pair distribution functions (PDFs) profiles obtained from high-energy X-ray experiments and ii) TEM particle size distributions data. Our package will generate an array of nanoparticles and fit their computed average PDFs to the experimental profiles. We’ll relax the structure of the nanoparticles on-the-fly to avoid exaggeration of structural disorder. We’ll then add other components of the system (ligands, water, and reducing agents) and perform reactive simulations of catalytic cycles to understand the structural evolution of the nanoparticles during reactions. Our computational studies will provide an accurate and unprecedented picture of the structure-function relationships in bio-inspired nanoparticles.

Research Experiences:

I started my research career as an experimentalist focusing on various aspects of polymer engineering including design of novel bio-fillers and optimization of hybrid nanocomposites. I then became a lead scientist in a biotech company to design polymer-based electrospun filters. My first theoretical research involved development of a new theory to predict interfacial tension of polymer blends. I complemented my theory with an easy-to-use application to conduct the calculations. After that, I officially became a computational materials scientist. My PhD studies mainly focused on development of new polarizable models and application of the advanced computational techniques to discover design principles of biological molecules for the growth of metal nanocrystals of controlled shape and size. I also developed predictive models of catalytic reactivity for those nanoparticles in various chemical reactions. These projects were accompanied with experimental supports from several research groups. In another computational project funded by industry, I developed the first models of specific carbohydrate polymer hydrogels for use in major healthcare products of Procter & Gamble (P&G) Company. During my postdoctoral appointment, I tackled some long-lasting problems in the amazing world of liquid crystalline materials. Here, in addition to ultra-large scale atomistic simulations (150K to 20M particles), I designed continuum level simulations using finite difference method. Additionally, I developed an accurate polarizable force field of calcium phosphates based on the classical Drude oscillator. I used quantum mechanical (QM) calculations and several experimental data to obtain and validate my force field. During more than twelve years of academic and industrial research, I was fortunate to work on several diverse experimental and computational projects. My extensive computational work has covered a wide range of length and timescales (nanometer to millimeter and femtosecond to seconds). I am confident that my diverse background and computational expertise have suitably prepared me to attack new emerging problems; the problems that their solution would improve human life in the future.

Teaching Experiences:

I first started tutoring students in math and physics when I was fifteen years old. Three years later, I officially became students’ mentor in my high school for nearly six years. During my graduate studies, I TAed, and then designed and taught the “Fluid Mechanics” course to senior undergraduate students for nearly three years. I also tutored a graduate-level course “Engineering Properties of Polymers”. I have also been the instructor of several workshops and have organized some local Chemistry Olympiad.

Teaching Interests:

I have a simple teaching philosophy: “Spend enough time to master the subject first. Then do your best to explain it to your students. If you do a good job, your students become masters but you’ll become a super-master; it’s a win-win scenario”. Since my official training is in Polymer Engineering, I am very comfortable with teaching various science and engineering courses including “Transport Phenomena”, “Process Control”, “General Chemistry”, “General Math”, “General Physics”, “Physical Chemistry”, “Thermodynamics”, and “Statistical Mechanics”. Because of my extensive computational research, I am able to teach general computer science courses. Furthermore, I will design and teach advanced computational courses including “Molecular Simulations of Materials”.