Dr. Siddhartha Das is currently an Associate Professor in the Department of Mechanical Engineering, University of Maryland, College Park. His research focuses on the science and engineering of soft and polymeric materials, interfacial transport, and small-scale fluid mechanics for fundamental discoveries (in ion dynamics at soft interfaces, liquid transport in soft-material-functionalized nanochannels, drop behavior on squishy surfaces, and charge-driven nanoparticle-lipid-bilayer interactions) and cutting-edge applications (in additive manufacturing). He received his B.S. (or B-Tech.) and Ph.D. from the Indian Institute of Technology Kharagpur. He has published more than 170 journal papers in world-renowned journals (such as Nature Materials, Science Advances, PNAS, PRL, JACS, APL, Matter, Nucleic Acid Research, Nature Communications, Advanced Materials, and ACS Nano) and has received numerous awards and accolades (including promotion to Associate Professorship with an early tenure, election as a Fellow to the Royal Society of Chemistry, Institute of Physics, U.K., and Institution of Engineering and Technology, U.K., Junior Faculty Outstanding Research Award of the A. James Clark School of Engineering, selection to contribute in the emerging investigator issue of the journal Physical Chemistry Chemical Physics and Soft Matter, IIT Kharagpur Young Alumni Achiever Award, Hind Rattan award).
Find out more about his work via his group’s Twitter @smiel_umd
Read Siddhartha Das’s Emerging Investigator article: http://xlink.rsc.org/?doi=10.1039/D2SM00997H
How do you feel about Soft Matter as a place to publish research on this topic?
Soft Matter is a wonderful venue for publishing exciting new works on soft materials, complex fluids, biological systems, etc. The present paper uses machine learning to unravel new characteristics of the water-water hydrogen bonds (HBs) inside the charged polyelectrolyte (PE) brush layer described using all-atom molecular dynamics (MD) simulations. Papers in these areas of polymer systems and machine learning applications of soft matter systems have been extensively published in Soft Matter due to the sheer reach and the visibility of the journal to the soft matter and polymer science community. In that respect, I consider Soft Matter to be a perfect place to publish my research.
What aspect of your work are you most excited about at the moment and what do you find most challenging about your research?
A big focus of my present research is to explore the properties and behaviors of polyelectrolyte brushes and brush-supported water molecules and ions using all-atom molecular dynamics simulations. Such all-atom simulations have been scarcely applied for PE brushes and my group was among the first to do so. Several of my previous papers have unraveled, for the first time, detailed properties and behaviors of PE-brush-supported water molecules and counterions. This present paper employs machine learning (ML) to take this endeavor further: ML enables us to identify structures and properties of water molecules and counterions inside the brush layer that are distinctly different from that outside the brush layer. In that way, we converge upon new definitions of water and ion properties inside the brush layer (e.g., the conditions that define water-water hydrogen bonds inside the brush layer). This is what I am really excited about at this moment: how the interplay of highly resolved atomistic simulations and machine learning algorithms enable us to obtain hitherto unknown properties of water and ions inside a PE brush layer. Such findings will lead to a paradigm shift of the way in which PE brushes are viewed by the research community: these brushes, henceforth, will be considered as a medium that triggers very rich water and ion science.
The most challenging part of my research is to connect these very interesting discoveries on PE-brush-supported ions and water to a larger scale description of the PE brush systems. Such a thing could possibly be accomplished by a multi-scale description of the PE brushes (where all-atom MD simulations and coarse-grained MD simulations are coupled) and ML methods will be useful for not only achieving such coupling but also for performing the sampling over a much larger time window.
In your opinion, what are the most important questions to be asked/answered in this field of research?
PE brushes have great potential for a multitude of applications in medical, chemical, engineering, and diagnostic sciences. The central tenet of these applications is the responsiveness of such brushes to environmental stimuli. These responses strongly depend on the structure of the PE brushes and the behavior and properties of the brush-supported water molecules and counterions. Given that we are now able to explore unprecedented atomistic details of such brush-supported water and ions, the most important questions to be answered in the field are as follows:
- How the properties of such brush-supported water and ion properties can be regulated for ensuring desired responsiveness of the brushes (and hence achieving unprecedented efficiency in certain established brush-driven applications and develop new applications of brush-based systems) to environmental stimuli?
- How to model reactive brushes and their responses to environmental stimuli?
- How liquid and ion transport take place at such brush-grafted interfaces?
- How machine learning can improve our predictions of all these effects?
Can you share one piece of career-related advice or wisdom with other early career scientists?
For early-career scientists, my one piece of career-related advice will be to have fun in doing new things in research. Very often the burden of the tenure-track system (or similar conditions) forces early career scientists to do research on things that are safer and on which they have previous experience. However, in that way, the fun of discovering new things and contributing to new areas goes completely missing and makes the life of early career scientists more stressful and the work less exciting.
