My Research and Study Journey

During my academic journey and professional career, I have had the privilege of engaging in diverse and enriching research experiences across different scientific domains. These experiences have shaped my passion for scientific inquiry and deepened my understanding of molecular and biological processes. In this research experience statement, I will highlight some of the key research projects I have been involved in and the valuable contributions I made to the scientific community. My research journey commenced during my undergraduate studies when I had the opportunity to intern at the Neuroscience Research Center at Dhaka University. Under the guidance of Prof. Dr. Mahmud Hossain, I was involved in a project aimed at analyzing neurotoxicity in the brain of a mouse model using molecular and behavioral analysis. This experience exposed me to cutting-edge research techniques and instilled in me the importance of meticulous data analysis and interpretation. Following my undergraduate s

          Next Generation of Quantum Algorithms and Materials Figure: Quantum Computer Simulations It is anticipated that quantum computing would fundamentally alter how researchers approach challenging computational issues. These computers are created to address significant problems in fundamental scientific fields like quantum chemistry. Quantum computing is still particularly sensitive to environmental disturbances and noise at this point in its development. As a result, quantum bits, or qubits, lose information when they become out of sync, a process known as decoherence, which makes quantum computing "noisy." Researchers at Pacific Northwest National Laboratory (PNNL) are creating simulations that offer a glimpse into how quantum computers function in order to get around the constraints of current quantum computers. According to PNNL computer scientist Ang Li, "the quantum states of quantum systems, like qubits, would collapse when we try to directly examine the act

Figure: A Model Structure-Neural Network Process Researchers from Los Alamos National Laboratory proposed incorporating more quantum mechanics mathematics into the structure of machine learning predictions in a new study published in Proceedings of the National Academy of Sciences. The machine learning model predicts an effective Hamiltonian matrix based on the specific positions of atoms within a molecule, which describes the various possible electronic states and their associated energies. The machine learning-based approach makes predictions at a much lower computational cost than traditional quantum chemistry simulations. It allows for quantitatively precise predictions of material properties, interpretable insight into the nature of chemical bonding between atoms, and can be used to predict other complex phenomena, such as how the system will respond to perturbations like light-matter interactions. The method also outperforms traditional machine learning models in terms of accurac

  One of the important emerging technologies for the twenty-first century is quantum computing. Even the most advanced supercomputers cannot match their potential. They have shown to be effective tools, especially for solving challenging computational problems that go beyond the capabilities of traditional hardware. Quantum chemistry is one interesting area for quantum computing, where it can be used to, for instance, solve the electronic Schrödinger equation and forecast the atomic composition of substances or molecules. Computer simulations are crucial in research to address these problems. On conventional computers, this is only partially doable with numerical approaches, though. Figure: Quantum Computer can revolutionize Chemistry Now that huge molecule simulations can be efficiently carried out on quantum computers, researchers at Paderborn University should be able to determine the energies and nuclear forces of these large molecules. The researchers concentrate on parallelizatio

The phosphodiester link between the tyrosine residue of topoisomerase I and the 3′-phosphate of DNA is hydrolyzed by the DNA repair enzyme tyrosyl-DNA phosphodiesterase I (Tdp1), which is conserved throughout eukaryotes. A fully quantum mechanical, geometrically constrained model is proposed and used to study atomic-level aspects of the Tdp1 process. The crystal structure of human Tdp1 inhibited by vanadate serves as the structural underpinning for the computer model (hTdp1, Protein Data Bank entry 1RFF). To gather thermodynamic and kinetic information about the catalytic pathway, including the phosphoryl transfer and subsequent hydrolysis, density functional theory computations are employed. A five-coordinate phosphorane intermediate associative phosphoryl transfer mechanism is suggested by the location of transition states and intermediates along the reaction coordinate. Similar to phospholipase D theoretical and experimental results.

Human histidine triad nucleotide-binding protein 1 (hHint1) acts as a hydrolase, breaking down substrates linked to phosphoramidites. To better understand the mechanism of hHint1-catalyzed phosphoramidite hydrolysis, DFT computational studies on a 228-atom cluster model of the enzyme were performed. The following steps comprise the overall proposed mechanism: (a) proton transfer from protonated His114 to form a protonated methyl amino group (2); (b) a protonated Penta-coordinated methyl phosphorodiamidate intermediate (3) formed by nitrogen attack of His112 on the phosphorus of the methyl phosphoramidite substrate via an associative intermediate; and (c) amine (RNH2) dissociation and formation of  (e) an interchange associative transition state creates a temporary tetra-coordinate phosphoryl intermediate (e) an interchange associative transition state generates a temporary tetra-coordinate phosphoryl intermediate (6); (f) the formation of a hydrolyzed nucleotide (7) that then transfers