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Research Projects

I am currently involved in five different projects. My research interests are quite interdisciplinary and diverse so that I tried to conduct research in a variety of subjects and tackle my enthusiasm from different directions.

Current Projects:

1) Investigating the effect of quenched and shot noise on soft active matter.

(April 2021 - June 2022 )

Supervisor: Prof. Tamas Vicsek

2) Evolution of Hyperuniformity on driven dissipative colloidal system

(August 2020 - March 2022)

Supervisor: Prof. Serim (Kayacan) Ilday

3) Fitness Landscape of driven dissipative colloidal system

(August 2020 - March 2022)

Supervisor: Prof. Serim (Kayacan) Ilday

4) Physics-driven machine learning Methods for characterizing the structure of materials

(October 2021 - January 2022 )

(Senior Project)

Supervisor: Prof. Hande Toffoli

5) Science of Success

(October 2021 - February 2022)

(Internship)

Supervisor: Prof. Albert-László Barabási

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1) Investigating the effect of quenched and shot noise on the soft active matter.

Publications:


Danial Vahabli, Tamas Vicsek, "The effect of quenched and shot noise in soft active matter" (In Progress)

In this project, we are investigating the collective motion of soft active matter in a medium with homogeneous and isotropic disorder by simulating the particles/agents with a set of well-defined rules. to be able to efficiently simulate a large number of agents and simulations We use CUDA (GPU/Parallel Computing Library by NVIDIA) Our results suggest that robust, complex, and rich patterns of motion could emerge from a simple set of parameters.

Possible Applications: Drone Control, Soft Active Matter Control
 

My Contributions to The Project:

This research is conducted by me and advised and guided by Prof. Tamas Vicsek as a part of my Erasmus + Traineeship Project. Here I have written Python Code using the CUDA library - a GPU/Parallel Computing Library provided by NVIDIA - which uses agent-based simulation methods to simulate the motion of agents following a set of Rules. Further, We analyze the agents' motion with order parameters, Auto-correlation Function, and MSD calculations to understand their behavior in more detail.

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Hyperuniformity on driven dissipative colloidal system

Hyperuniformity is a measure of Long-Range Order in a wide set of systems. the measure is first introduced by Salvatore Torquato and Frank Stillinger in 2003 and they had shown that among other things, Hyperuniformity provides a unified framework to classify and structurally characterize crystals, quasi-crystals, and the exotic disordered varieties. (Wikipedia)

Although the experimental and Theoretical studies are vast in the subject, there is little to no research in studying the dynamics and the evolution of Hyperunifiormty in an experimental system so that we decided to study its evolution in our driven dissipative colloidal system.

My Contributions to The Project:

I contributed to different aspects of this project. Firstly, I helped to develop a computational toolbox using MATLAB which analyzes our experimental results on the fly which is used in the published paper. Secondly, I am using agent-based simulation methods both on MATLAB and Python using both the CPU and the GPU to simulate the dynamics of our experimental results via simulating the particles (Hard-Sphere Simulations) and the Fluid. (Fluid Dynamics) to study which factor is exactly responsible for the emergence of hyperuniform states.

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Figures from Published Article

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Artist representation of a small portion of the proposed fitness landscape. Courtesy of Dr. Ghaith Makey.

Fitness Landscape of driven dissipative colloidal system

this research scrutinize the fundamental question at the heart of the condensed matter, statistical and nonlinear physics: When far from equilibrium, in the presence of fluctuations, and faced with multiple steady states with small energy differences, what are the relative probabilities that the system evolves to each accessible steady state? Our goal is to create a phase map of these colloidal crystals, similar to a phase diagram of thermodynamics, but where each phase (here, crystal pattern) is dynamic and of finite occupation probability. We will use a convenient tool, fitness landscapes, which originates from evolutionary biology to describe the stability of each phase in various conditions. We will further ask to what extent this control is extendable down to the few-nm scale, where fluctuations are much more substantial than 500-nm polystyrene colloids, and if and how these findings change when using nonidentical, in size or shape, but still passive particles? (retrieved from Simply Complex Lab's Website)

Publications:


In Progress

My Contributions to The Project:

To understand the dynamics of our system and the emergence of the lattices we first need to be able to detect the particles and their class with perfect accuracy, to reach this goal we use 2 different machine learning algorithms but we need to tune and train the machine learning algorithms and that requires a set of artificial images which I am generating using agent-based simulations and lattice vector definitions.