Computational Materials Science

I received a call from Dr. Khalid Mahmood Ghauri today. Dr. Ghauri taught me at the University of engineering and technology, Lahore when I was an undergraduate student. He is one of my favorite teachers. I still remember and respect his ideas about life and work. So as I received his call after a long time, I naturally felt very happy and highly obliged. Dr. Ghauri talked to me about work and stuff again. And it was good. He advised me that we could work together on common areas of interest. So in that vein, I am listing over here some ideas that marry computer science with materials science. And even though I have been away from materials science for a long time, I still think that there are certain things that could be done together in an interdisciplinary project.

Computational materials science is a hot domain that, broadly speaking, allows innovation in the field of materials science through computational methods. What inspires me most about this is the realistic simulation of new materials that could be used revolutionize some industries, such as renewable energy. I have been writing about solar simulations before in which I pointed at tools that could be used for simulation of solar energy panels. Coupled with machine learning techniques, realistic simulation software can help us achieve higher strides in the domain of materials science.

I have been fascinated by other simulators that can help us in novelty detection in materials science. One such tool is Quantum Espresso. This is based on density functional theory and allows simulation of newer materials that would be hard to invent by hand and experiment in a lab.



Quantum Espresso is already being coupled with machine learning to create novel materials. Here are a few links to published work.

EVO-Evolutionary algorithm for crystal structure prediction – ScienceDirect

EVO successfully demonstrates its applicability to find crystal structures of the elements of the 3rd main group with their different spacegroups. For this we used the number of atoms in the conventional cell and multiples of it.

[1610.08106] Evolutionary optimization of PAW data-sets for accurate high pressure simulations

Abstract: We examine the challenge of performing accurate electronic structure calculations at high pressures by comparing the results of all-electron full potential linearized augmented-plane-wave calculations with those of the projector augmented wave (PAW) method.

I advocate about a theme that I believe is very suitable for innovation. In this theme, I argue about gluing domain-specific realistic simulators with machine learning algorithms. If you scroll back and read again my article you will find very vivid hints about this theme, even if you ignored them before. If you want to develop a profound understanding of this idea, please read the following article.


Simulators as Drivers of Cutting Edge Research – IEEE Xplore Document

Undertaking engineering research can be compounding for beginning graduate students and thwarting even for seasoned researchers. With a wealth of academic

The phone call from Dr. Ghauri made me a day.

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CC BY-NC-ND 4.0 Computational Materials Science by Psyops Prime is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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