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Harender Dhattarwal
about Harender
Harender S. Dhattarwal is a postdoctoral research associate in the group of Prof. Richard Remsing at Rutgers University. Harender received his B.Sc. in chemistry from the Ramjas College, Delhi University in 2015. He then went to the Indian Institute of Techonology Guwahati for his M.Sc. in chemistry in 2017. He then received his Ph.D. in Chemistry from Indian Institute of Techonology Delhi, India. During that time, he conducted research in the lab of Prof. Hemant Kashyap in collaboration with Prof. Jer-Lai Kuo at IAMS, Taiwan. His graduate research focused on understanding the structure and stability of modern electrolytes in confinements and near interfaces.
Harender S. Dhattarwal is a postdoctoral research associate in the group of Prof. Richard Remsing at Rutgers University. Harender received his B.Sc. in chemistry from the Ramjas College, Delhi University in 2015. He then went to the Indian Institute of Techonology Guwahati for his M.Sc. in chemistry in 2017. He then received his Ph.D. in Chemistry from Indian Institute of Techonology Delhi, India. During that time, he conducted research in the lab of Prof. Hemant Kashyap in collaboration with Prof. Jer-Lai Kuo at IAMS, Taiwan. His graduate research focused on understanding the structure and stability of modern electrolytes in confinements and near interfaces.
Research Interests
Research Interests
Developing Machine Learning Potentials
Developing Machine Learning Potentials
My research focuses on developing and testing advanced machine learning-based neural network potentials to bridge the gap between ab initio accuracy and large-scale molecular simulations. I am particularly interested in creating models that accurately capture long-range electrostatic interactions, and applying them to study complex molecular systems. My work explores how these interactions influence short-range atomic environments, especially in polar liquids like water. A key aspect of my research is ensuring the transferability of these models, enabling them to predict nonlinear responses, such as dielectric saturation, even in regimes beyond their training data. By combining machine learning with physical insights, I aim to push the boundaries of computational chemistry and materials science, with applications in understanding biological systems, electrochemical processes, and the behavior of liquids under extreme conditions.
Energy Storage Materials
Energy Storage Materials
I am deeply engaged in designing next-generation materials for energy storage applications. My research focuses on solid-state electrolytes and thermochemical energy storage systems, where I aim to uncover design principles for materials with enhanced performance and stability. A key aspect of this work involves using quantum dynamics simulations to identify hidden electronic disorder in these systems, which can significantly impact ion transport and energy storage efficiency. By understanding the atomic and electronic structure of these materials at a fundamental level, I strive to develop innovative strategies for optimizing their properties, such as ionic conductivity and thermal stability. This research has the potential to advance technologies like solid-state batteries and thermochemical storage systems, contributing to the development of sustainable and efficient energy solutions.
Complex Liquids
Complex Liquids
I am also passionate about studying complex liquids, such as ionic liquids and water-in-salt electrolytes, to explore their applications in energy storage, carbon capture, and beyond. My research focuses on understanding the structure-property correlations in these materials, particularly how their unique molecular arrangements influence key properties like ionic conductivity, electrochemical stability, and solvation behavior. I am especially interested in investigating how these liquids behave in real-world scenarios, such as under confinement or at high electrochemical potentials, where their structure and stability can significantly impact performance. By combining advanced computational techniques with experimental insights, I aim to uncover the fundamental principles governing these systems, paving the way for their optimization in cutting-edge applications like next-generation batteries, supercapacitors, and sustainable carbon capture technologies.
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