(3dh) Computational Molecular Science: From Biology to Nanotechnology and Beyond | AIChE

(3dh) Computational Molecular Science: From Biology to Nanotechnology and Beyond

Authors 

Patel, A. J. - Presenter, Rensselaer Polytechnic Institute


Computational science has been invaluable in helping us understand the molecular basis of diverse phenomena across various disciplines, ranging from nanotechnology to biology. Water-mediated hydrophobic interactions are ubiquitous in biology, yet faithfully describing them has been challenging, due to their inherently non-additive and context-dependent nature. We have recently developed a technique that enables quantitative characterization of these interactions, and will enable the study of a diverse class of problems ranging from amyloids, to protein targeting, and drug discovery. With the tremendous growth in computing power now available and the recent development of enhanced sampling techniques, we on the verge of being able to use computation as a tool for guiding the search for novel materials, with applications in water purification and energy storage. Nano-structured materials, including carbon nanotubes, are promising candidates to tackle the problem of water purification in an energy-efficient manner. Increasing interest in this field has led to a rapid growth in the available chemistries with which these nanotubes can be decorated. However, we need to understand the water-mediated interactions and identify the underlying principles of ion solvation and exclusion in confinement, in order to inform the optimal design of these advanced materials. Another problem in which ion solvation and transport also plays a key role is lithium-ion batteries. Novel polymer electrolytes provide the promise of delivering a lithium battery with high energy-density that could revolutionize the way we consume energy. However, judiciously chosen candidates need to be examined, using state-of-the-art modeling techniques in order to guide the search for the optimal electrolyte material. I propose to employ a combination of statistical mechanics and molecular computation to address these challenging problems that span a wide spectrum of time and length scales.