Hello.

Nice community Arjun.

For this interview, we have to continue in en-US. Could you please begin by introducing your professional journey in academia?

Sir, yeah, I completed my PSD from Indian Institute of Technology Monday in in 2018 and thereafter I did a couple of post docs including the first post doc I did from 2018 to 2020 in.

And I followed by 1 1/2 year postdoc in Masarik University, Bruno, Czech Republic. And then I, I was as a postdoc.

India.

In Oregon Health and Science University.

The uh, for, uh, two years and uh.

Thank you for sharing your academic journey, Professor.

Let us begin. Professor, can you start by bringing me briefly up to speed on your academic background and expertise relevant to computational physics? Please share the key highlights.

Sure, sure. So, uh, from the computational side, I did my PLD in computational polymer physics where I, uh, wrote uh, uh, codes in CC plus plus, uh, for protein and dynamics modeling, which is.

Likely the polymer physics. During that time I I learned coding as well as using.

On statistical mechanics to analyze uh results uh, and the coding continues uh thereafter I umm, I uh, have been coding since uh then so computational umm uh is always there with me I.

Simulated various systems, including colloids, uh, polymers, umm, and, uh, biological, uh, systems like, uh, I worked on, uh, cellular system and uh, they're also I, I was quoting in Python, uh, I. So my background from the computational side is I know Python CC plus plus and.

That I use to simulate various kind of physical systems.

Understood, Professor. Thank you for sharing this overview. Now, let's delve deeper into your computational physics expertise. Specifically, could you outline how you've applied computational techniques to advance materials research, perhaps focusing on polymers or colloids you mentioned earlier?

So let me start from the polymers. So during my PSDI.

So where I got, uh, the system, uh, trajectory in time. And then those trajectories, umm, are analyzed using the concepts of statistical mechanics to, uh, understand, uh, uh, for example, stroke science team and device stroke science relations in polymers, uh, near the class transition that was during the PhD and then thereafter, uh, doing the. Uh, post doc? First post doc in India that was.

I wrote codes for polymer simulations using the technique Molecular Dynamics, Monte Carlo and Brownian Dynamics.

Umm, I simulated the bacterial properties, which is the quorum sensing, umm, uh, using uh, the simulations again, uh, of the colloidal particles, uh, which are considered as a spherical bacteria.

And that, and additionally, then I worked on that project, which was on the colloids where, uh, the colloidal motion over the external potential barrier was studied and.

Umm, And we got, uh, nice result, which were then, uh, published in, uh, general general is condensed matter, which is a nice physics general.

Appreciated. It’s clear you've applied computational techniques across a spectrum of physical systems. Let’s shift focus slightly to condensed matter and quantum materials. Can you discuss an example where you delved into computational modeling to address problems or phenomena in these domains?

Sure, Sir. So, uh, for example, during my last postdoc where I worked on a generative AI model, uh, for protein dynamics, uh, so since.

For protein simulations.

Which are based on all item, uh, simulation. And, uh, those are the force parameters I derived from, uh, quantum mechanics, umm, uh, calculations. So I simulated, uh, various proteins, uh, and collected their molecular dynamic simulation data. And then using that data, I trained machine learning model.

To generate protein confirmations.

So so that that could be actually.

Related to what you are asking for quantum systems.

That aligns well, Professor. Utilizing quantum-mechanical-derived parameters for force calculations is a fundamental computational approach. Combining molecular dynamics data with machine learning models for protein conformations is an intriguing overlap between quantum systems and advanced computational techniques. Now, given your expertise in teaching, how do you typically introduce computational physics concepts to students with minimal coding or modeling experience?

Sir, Sir, so I have one year of teaching experience as well where I taught students Bachelors of Science and Masters of Science. There also I taught this computational physics course as well as in theory as well as in lab. So there we wrote a simple codes for example. Simple harmonic motion. That is the simplest code 1.

Can write and simulate the harmonic oscillator to get students know how this works in practice.

That's a practical and structured approach, Professor. Introducing students to foundational concepts like simple harmonic motion through basic coding aligns well with making computational physics more approachable. How do you evaluate students' understanding and their ability to apply these computational methods in both coursework and lab settings?

Sir, Sir, so to. Analyze. This so from the physics point of view.

Students should know the basic concepts behind simple harmonic motion.

Which is the force is directly proportional to negative of displacement, which is the first thing they should be knowing.

And then from the lab perspective, they, uh, should be knowing how, uh, to write, uh, very simple code which can actually generate, uh, that motion and like different displacement it can.

Give uh, which can be later plotted, uh, with respect to time, like how it uh, actually goes.

Thank you, Professor. Your emphasis on connecting theoretical principles with practical application and visualization strikes a good balance for evaluating comprehension and practical skills in computational physics. Let’s shift to guiding student projects. How do you ensure that undergraduate or graduate research projects under your supervision remain both rigorously scientific and pedagogically enriching?

Yeah. So, so that is uh, quite, uh, impressive question and which is also, umm, exciting for me to answer. Umm.

So the thing.

I would like to.

Work for the research purposes on very recent hot topics, for example recently this use of generative AI modeling.

For protein dynamics, which is directly relevant to drug discovery, which is actually very important problem because as this drug discovery.

Umm, area will advance that will actually help, uh, people, uh, get rid of any diseases including cancer, umm, uh, diabetes and so on in uh, and.

Even, uh, this, uh, longevity drugs are also people are actually working on.

So yeah, those are directly areas I would like to work and I will, I would like to guide them so.

They should go through a recent literature, what literature says, what are the actually gaps in the literature and what are the things which need to be done and then start working on the those new things to explore.

So for the starting point, umm.

Mm-hmm.

Have contributed to advancing the field of computational physics? Specifically, what unique insights or methodologies have you introduced through your work?

Sure Sir. So for example let me start from my PhD. So in PSD I worked on this polymer glass where these polymers show glassy type behavior and since.

Umm, uh, this, uh, glass, uh, is uh, qualitatively, umm uh, similar.

In upholding problem where glasses also have this rugged free energy or potential energy landscape which is the same in case of protein upholding.

So, and, uh, during that, uh, my PhD time, I, uh, worked on this, umm, violation of stock science scheme and stoke science in device relations, uh, in polymers, uh, during their class transition. Which is very impressive.

And that that will actually help to understand protein folding problem which is still unsolved.

Work which says that the violation occurs near the glass transition and which is due to the.

Mobility disparity like what happens is that some of the particles which are mobile, some are in mobile means some are moving faster, some are moving slower. So there is a disparity which is actually.

Related to this.

Wireless in all these relation which is directly.

Related to protein folding problem and then I during my first post doc I worked on this quorum sensing problem which is again a very important problem so we developed a new novel model for quorum sensing in bacteria so as. There are actually bacterias.

Which, uh, show this quorum fencing and that makes, uh, us sick. So, uh, in that paper, I um, actually, uh, there was, that was a 2 fold paper, like a new model to understand the phenomena and, um.

Then understand its uh properties like phase separation dynamics.

And further on then I, uh, worked on, uh, these, uh, cell biology project, umm, uh, which is, uh, again related to cancer, uh, where I developed generative AI model to get a cell cycle dynamics. As we know that there are actually drugs which are more.

Impactful when uh they are actually have they have given during certain cell cycle.

Duration of those particular sales which we are targeting for example in tensor. So yeah, that is.

There and the the recent one is 11 to drug discovery. So I actually worked on very range of topics.

From a very course and.

Approach to all item uh and uh. It is uh impactful in umm.

Polymers to colloids and to, uh, the proteins.

Thank you, Professor, for such a nuanced overview of your research contributions. It's evident that your work spans a wide range of impactful topics, from polymer behavior and glass transition investigations to quorum sensing and cancer-related cell cycle dynamics—each showcasing innovative modeling and computational approaches. These contributions clearly demonstrate academic depth and relevance. Next, could you elaborate on any consultancy or industry-facing projects you've undertaken? Specifically, how have you applied your computational physics expertise in such settings?

Yeah. So that is an important question. Yeah, I would like to actually collaborate with industry on this drug discovery project which is a hot topic. And many industries they are actually looking forward to the to help these drug companies to make more potential drug which is actually.

Useful with very low doses.

Like pico molar on nanomolar, uh, doses. So yeah, that project I would like to do with industry.

Understood, Professor. Collaborating with industries on drug discovery at nanomolar or picomolar dosing levels reflects a real-world application of your computational expertise in addressing critical challenges. Let’s confirm one crucial detail to conclude this segment. Do you hold a PhD in a specialized domain relevant to computational physics?

Yes.

Thank you, Professor. That confirms your credentials. We have thoroughly assessed your technical and academic expertise across all required areas.

Mm-hmm.

Siri, Sir.

In

Professor, let me ask—what is a foundational physics concept that you particularly enjoy teaching to students, and why?

Yeah, that is a great question and I would like to teach and statistical mechanics which is very foundation and and nowadays it is applicable more or less everywhere including in drug discovery thing in even finance and economics everywhere. So statistical mechanics is the course which like even across the disciplines.

In physics, chemistry and biology, uh, people, uh, need, uh, to, uh, work on these, uh, problems like cancer, umm, or, uh, the drug discovery projects.

Interesting perspective, Professor. I'll save further discussion for now.

Mm-hmm.

Mm-hmm.

Your voice is little, uh, disconnecting I guess.

Mm-hmm.

I I can't hear you.

Mm-hmm.

Mm-hmm.

Could you elaborate on your Ph.D. research at IIT Mandi and its implications for computational physics?

Siri, Sir.

So, uh, during my PhD in IIT Monday, I umm, uh, developed, uh, a molecular dynamics Monte Carlo and Downing dynamic codes to simulate, uh, polymers, uh, colloids, uh, and so on. And uh, which is uh, actually uh, like writing codes from the scratch, so. There I learned how to write codes for physical systems.

And then on.

Yeah.

Yes, yes.

Thank you for confirming. How do you incorporate your expertise in computational simulations, such as coding for physical systems, into your approach for teaching complex concepts in computational physics?

Sure. So to apply those like if you if someone knows basic coding and the how to code for physical systems, then that is actually applicable to real-world case scenarios as well as to understand the basic physics for example. In case of understanding how these polymers actually.

I behave in synthetic sense or as well as since we know proteins, RNA, DNA, all these are polymers so that will be applied to these case scenarios as well.

That's insightful, Professor Singh. Could you explain how you would guide a student with minimal coding experience to build foundational skills for simulating physical systems, such as polymers or proteins?

Yeah, so. Basic of molecular dynamics, for example is.

One should start from the initial configuration. So for example if I have a polymer so.

They should be.

Started with any random configuration, for example. Random positions of the particles of these beads connected through the covalent bonds.

Uh, put all the those into the box and then, uh, apply periodic boundary condition, uh, and followed by then, uh, uh, calculate the courses, uh, and there should not be any overlap in the beginning. And then move the particles with algorithms like velocity bullet algorithm, which I used. So, and that, that's how actually the simulation goes like initial configuration plus post calculation plus move and then.

Will actually go on and depending on how long you wanna simulate a system.

You provided a detailed process for beginners to simulate physical systems—starting with random configurations, applying boundary conditions, and using algorithms like velocity Verlet. How would you make this method accessible to undergraduate students with less experience in computational physics?

Yeah, that's nice. So there is a book actually, uh, uh, computer computer, uh, simulation, uh, uh, in liquids by MPLN and DJ Tilde C So that book has all these things for the beginner. And there is another book, Understanding Molecular Simulation by Dan Frankel. So these two books are the basic books, I think, uh, uh, for a beginner to start with. They explain every basic things and basic coding.

Also there and some actually codes are also there to understand.

You've highlighted foundational resources like *Computer Simulation in Liquids* and *Understanding Molecular Simulation* as effective starting points. How do you ensure students not only learn from these resources but also apply the methods practically in coding and simulations during your classes?

Yeah. So, uh, during classes, I would like to actually, umm, uh, teach them, uh, basic physics for behind, uh, these systems, for example, polymers, uh, uh, from the concepts of statistical physics.

Thank you, Professor Singh.

Professor, let's begin this section by discussing your three-year research roadmap. Specifically, how do you envision your work in computational physics contributing meaningfully to research output and institutional rankings?

Sure. So as I illustrated previously, I would like to go ahead with uh, this, umm, uh, protein dynamics, uh, problem, uh, since.

Like the protein structures, right now we have from even either from protein data bank, we have only single or a few structures or even from these experiment for example NMR or cryo EM. But these proteins are the dynamic entities, they move a lot and they have.

Because as we know, even though we know these drugs are working somehow, but they often bind to off target and that is because we don't know too many different these configurations of the proteins. So my. This long term goal would be to make a generative model that can actually give these different protein conformation, which would be essential for.

Plethora of conformations and those conformations are very important for these drugs.

And drug discovery.

Problem and making actually drugs more efficient and reachable to the public in.

Less cost.

Understood, Professor. You've outlined an ambitious focus on using computational approaches, likely machine learning or structural modeling, to understand and utilize protein dynamics in drug discovery. How would you ensure scalability in your research lab, particularly when it comes to mentoring junior researchers and fostering both high-quality publication outputs and skill development?

Yeah, that is the important part. So the thing how I would like to do is.

So since in a group which is in the starting phase, so I I would like to engage more and more with students on daily basis and take their. Input and give them feedback as soon as possible so that.

Uh, they advance actually, umm, uh, in uh, right direction.

I see. Lastly, looking ahead, how do you plan to leverage international collaborations within this field to enhance the visibility and citation impact of your research?

Yeah, that is an an important question as well. So I as you know I worked around the globe in Czech Republic, in US, two postdocs and in India as well. So I have a couple of contacts of the professors who are actually working in this area of the discovery. So for example, during my Last Post doc. I worked with Professor Ben Brown and.

Professor Dan Zakarman So I would like to actually collaborate with them to work on this problem. So that will actually make me internationally visible among the community.

Thank you for your insights, Professor. Let's proceed.

Is that?

And.

Mm-hmm.

Could you explain how you would implement an active learning model, such as the Flipped Classroom, for a large-enrollment computational physics course without relying on slides?

Yeah. So in case of active learning, I would like to come up with my handwritten notes. For example, I am teaching for the basic course of multiple dynamics. So I would be having my handwritten notes and I will start from the very basic what molecular dynamics means. So like what is the meaning of dynamics and what what are the? Guys I am looking for like for example molecules.

So I will start from there, uh, and, uh, start from a Newton's equation of motion. So these are the basic, uh, umm, concepts. I will start, uh, without slides.

I see. Thank you for that explanation, Professor. Could you elaborate on how you approach teaching complex computational physics topics, like quantum materials modeling, in a way that is engaging for both undergraduate and postgraduate students?

Time is up! Ending the interview now.