Good day, Dr. K. Vijay Reddy. I'm Arjun. I've had a look through your background and your professional record. I'm interested in hearing more about your work and how you see yourself fitting in here with our faculty. Ready to start?

Please start by introducing your professional journey in academia.

Hello, good morning.

My name is KVJ Reddy and I have done my Masters and PhD from National Ministry of Technology, Rahulkilla, Odisha and my area of expertise.

Thanks for sharing that!

Dr. Vijay Reddy, let's begin by verifying your expertise for the Computational Modelling Professor role. We'll focus on a sequence of skills, one at a time. Starting with computational modeling: Could you explain your approach to developing a computational model for predicting material properties under extreme conditions?

Yes, We can study the material property at extreme conditions using molecular dynamics simulation and cellular automata, where we will study the high temperature implications of the materials and we'll study the heat treatment process. We can study the heat treatment processes and how the mechanism will be, how the material will behave at high temperatures, mostly the dislocation effect evolutions and the defect analysis and the fracture mechanism. We can study all that, all the three.

Properties and we can also study the structural mechanisms such as the tensile behavior and the fracture mechanics and material property.

So actually what what I do is we study molecular dynamics simulations where we use interatomic potentials to.

To to detect them to define the atoms like materials. If we are using aluminium and nickel, we have the specific inter atomic potentials and the inter atomic potential itself is developed using fundamental techniques such as density functional theory. And with that we can study the interactions between the aluminium aluminium atoms and aluminium nickel. As an example I'm giving so.

With that we can study that mechanical properties and thermal properties and also vibrational properties. So apart from that we also use experimental techniques where we validate the tensor like if we are using tensile methods then we can validate the yield strength and also the matrix properties such as Young's modulus, all that behavior.

Thank you, Dr. Reddy. Your explanation demonstrates a solid understanding of validation through both simulation and experimental methods, integrating molecular dynamics with experimental validation for mechanical properties. Next, focusing on the "Application of AI/ML to Materials Science and Manufacturing," can you describe an example of using AI or machine learning to enhance materials design or analysis?

Yes, recently AI and ML has been a very integrating technique that is used with DFTS and molecular dynamics simulations for understanding the material behavior because molecular dynamics is a very nanoscale.

Atomic simulation. So if we use AI and ML, because we can generate a lot of data from molecular dynamics, that data can be used to, uh, integrate with AI and machine learning. And because there are a lot of libraries that we have and we can use the algorithms to study or extrapolate the properties to a higher scale. If we are studying at a 20 nanometer or 30 nanometer scale, then we can use AI and ML to enhance or to increase extrapolate the properties to at least 100 nanometers.

Or even more so with that, we can, uh, like random forest, uh, algorithms are there regression methods are there so we can use all that, uh, methods to, and since we produce a lot of data, so that data can be used and put in the algorithm to study materials behavior at a higher scale.

Yes.

Following up on that, how do you ensure the quality of the data generated from molecular dynamics and its compatibility with AI/ML models during this process?

So actually the data we generate is a lot of fundamental science that values we generate has repeatability that we can generate multiple simulate, we can run multiple simulations and generate data that can be validated for its repeatability. So once we do that, we can be sure that this data is valid and can be used for AI and ML. Also we cross check with experimental data that what we have generated is correct or not correct. So for that we.

We need good collaborations between experimentalists and simulations. With that we can use the data to validate the data to use it for the AI and ML model. In fact, we have already done that. Like in few of my research papers, I have used the data from the simulation and used it for extrapolation. Extrapolation in AI and ML models.

So basically I'm doing molecular dynamics and I also have expertise in cellular automata. So in cellular automata we use MATLAB and for molecular dynamics we use Python based codings. So coming to cellular automata, the MATLAB codes we use are like we create matrices, multiple matrices and then we overlap those matrices with properties. So and then we study. We then use a for loops or if loops so we can.

Understood. Ensuring repeatability and collaborating with experimentalists for validation reflects a solid methodology for data quality assurance. Let’s move to assessing your proficiency in computer programming and computational analysis. Could you describe how you typically utilize tools like MATLAB or Python in your computational work?

We we can extrapolate the data from from the matrices and then we can, uh, like run the, run the simulations or run the loops to.

Like.

Increase like to increase the values, increase the values of those matrices and then resulting in a property analysis coming to Python based coding in molecular dynamics. So the Python codes we use are already in like fed in the lambs code like Python is the base background code. And then on top of that we use a platform called lamps. So which is based on Python. So we use the lamps code to run the simulations to run multi dynamic simulate like the.

The properties for the extreme temperature properties, to calculate those we use the simulation and also we use orbital for visualization which is also a Python based coding. And then we can visualize the atoms, the dislocations and the atomic strains in strains and stresses that are generated in the material using those Python codes.

Your explanation illustrates a strong command of applying MATLAB for matrix-based cellular automata simulations and Python-based tools like LAMMPS and OVITO for molecular dynamics and visualization tasks. Regarding teaching and engagement, how would you approach explaining fundamental computational modeling concepts to undergraduate students in a classroom setting?

So in perspective of teaching, I'd start with very basic codes like there are tutorials that are available. So I'll start with basic fundamentals of the coding, the basic architecture of how to write the code and then very simple codes that that would run in approximately 5 to 10 minutes so that we can get very quick results. And with that we can start like increasing the toughness or the.

In this perspective, I also have a book written that can help in understanding how to like how to go on with molecular dynamics so.

Complicated, complicated reports and then.

And also for the for the like fundamental understanding with.

Yes. So with that.

Merge. I would like merge the fundamental understanding with computational codes.

I would.

It seems you take a structured approach, beginning with fundamental coding principles and gradually advancing in complexity, complemented by theoretical insights. Merging computational exercises with foundational concepts is indeed effective for undergraduate teaching. Let me now assess your experience guiding student projects and evaluations. Could you share an example where you mentored students, either in research or coursework?

Hello.

Yes, Professor. Could you provide an example of when you mentored students, either in research or coursework?

Yes, I have mentored students when I was in PhD. So I was working with bachelor students and master students with whom I.

I, I have given some problems like research problem statements and then we have worked on that. I have mentored them how to proceed with the problems and then we have also published papers like research publications. So yes, I have mentored a few students in my PhD and during my even during my post doc I have worked with PhD students and mentored them with experimental simulation based studies.

That demonstrates valuable mentoring experience across different academic levels, both in research and experimental simulations. Moving forward, could you elaborate on how you structure the evaluation criteria for students' coursework or project assessments to ensure fairness and rigor?

Excuse me, can you please repeat the question?

Certainly. How do you structure evaluation criteria for students' coursework or project assessments to ensure both fairness and academic rigor?

So for for assessment, teaching assessment we will have like tests and assignments and classroom interactions, how the student behave in the class and how he responds to like.

To questions and to like classroom teachings in case of research we would like I would like to mainly.

Like start some sessions like technical sessions or quiz competitions where the student will use their fundamental understanding along with computational techniques so that I can assess better how the fundamental. Because for the simulation we need a strong fundamental basis. Without the fundamental basis we cannot code like simulate many of the.

Processes like deformation processes or defect analysis. So for simulation to occur, we should have a very good fundamental understanding. So from that I would assess that how good a student is on the fundamental basis and based on that I would like suggest him or her to like upgrade or to study which areas are weak or which areas are strong. I would assess that.

Your approach includes diverse evaluation methods—tests, assignments, and technical sessions—to gauge both theoretical and computational understanding. Tailoring feedback to identify and address specific weaknesses reflects a robust evaluation framework. Turning to research publications, could you highlight a particular publication of yours and its significance in the field?

So the most recent publication we did was in general of alloy, compound and medial chemistry physics, where I worked on high entropy alloys and this the structural.

High entropy alloy where I uh, I did tensile testing with the high percentage of B2 phases, B2 phases, sorry, usually hard phases and the normal uh, uh, high entropy alloy is the FCC phase, which is a software phase. So increasing the B2 phase has given me has allowed increase in the strength and like hardness. But with, with harder phase, like V2 phases, the toughness of the specimen decreases. So there is a conflict of increasing the strength and decreasing the toughness of the.

The how the structure in the alloy defines the mechanical properties. So I have studied Al, Nico, CR and.

Specimen. So I have a given an optimization like optimization structure where we'll have intermediate particles of V2 phases and a large.

Like larger part of F, uh, FCC phase where we can, uh, uh, optimize the rise in the strength to decrease in the toughness. So that was a very recent paper that was published in Sci journal Material Chemistry and Physics.

Thank you for sharing. That research, focusing on high entropy alloys and optimizing the balance between strength and toughness through structural adjustments, demonstrates meaningful contributions to material science. Now, as part of the role, industry collaboration and consultancy projects are significant. Could you recount an instance where you contributed to an industry-oriented project or consultancy related to your expertise?

Yes. So this was during my early stage early career where I started the PhD. So we collaborated with Midhani Midani group of like it's a it's a research industry where they develop metals and materials. So there we have studied the how steels and like low carbon steels developed during the heat treatment processes. So that was a collaboration we did with Midani and I studied the.

Dual fest deals actually where the austenite and the finite phases are present. But austenite is a software phase again and finite is a like harder phase and gives strength. So we studied how during the heat treatment the microstructure develops, how the Faraday transforms into austenite and then austenite again regains this faradic values. So that was the one of the study where we have collaborated with the industry.

It’s commendable that you collaborated with a research-driven industry like Midhani to investigate dual-phase steels and their microstructural transformations during heat treatment. Such expertise in balancing microstructural properties is highly relevant to this role. Having reviewed all the pertinent areas, Dr. Reddy, we're concluding the verification of your competencies. Would you like to continue discussing any additional topics or conclude this interview?

University of California, Irvine. So there we have worked on various topics, multiple topics. And I have a good research collaboration experience with international professors as well as in in India, like where I've been working in NIT and in UPSC. Yeah, in the.

Actually, I'd also like to say that I've also collaborated with the international professors like University of Manitoba and University of Florida and then.

CSR, IIP. So yes, I have a vast collaborative experience both in India and abroad.

Thank you for highlighting your international and domestic collaborative experiences, which strengthen your research profile significantly. It demonstrates an ability to contribute to global and interdisciplinary projects—highly valuable for this role. Do you have anything else you’d like to share before we conclude?

No, thank you.

Yes, you you can proceed.

Thank you, Dr. Reddy, for your time and thoughtful responses during this interview. You've provided a thorough overview of your expertise and experiences. If you're ready, I can finalize the interview. Would you like me to proceed?