Metallurgy is more than 1000 years old; in the past, it was treated more like a craft than a Science. But in the last century, it has become one of the dominating fields in Science and Engineering. Various materials, including metals and alloys, are studied and used for various engineering applications. Therefore, the term “Materials Science” is more widely used.
With the advent of modern computers and the exponential growth in computing power, scientists and engineers from various fields have developed an interest in the computational approach to solving problems in conjunction with theoretical and experimental studies.
Computational Science has also found its way into the field of Materials Science. In this blog, the field of Computational Materials Science is highlighted, and various results from computational research are reviewed to create awareness and provide insights necessary to appreciate the beauty and importance of this domain.
Density of States (DOS) Calculation Workflow – Curated Using Perplexity AI
Plotting the Density of States (DOS) from Vienna Ab initio Simulation Package (VASP) calculations using the Atomic Simulation Environment (ASE) is a crucial task in computational materials science. This process involves extracting electronic structure data from VASP outputs and visualizing it to gain insights into material properties. The workflow typically includes running VASP calculations, processing…
Unveiling a New Horizon: Predictive Science Based Multiscale Modeling and AI Tools for Accelerated Materials Discovery
Developing new materials for critical applications requires time-consuming testing and experimentation to determine their long-term performance and integrity. Materials used in extreme environments require long-term reliability, as repair is often not feasible. Traditional materials discovery workflows based on a trial-and-error approach must be replaced by a more efficient data-driven process that could accelerate the deployment…
The Essence of Scientific Computing
Scientific computing is the process of computing data to describe a scientific problem, which falls under the broad domain of computational science. This rapidly growing branch is an amalgamation of various branches of Science and Engineering and covers the various aspects of a complex problem. Although computational science spans many different disciplines, at its core,…
Mayavi: Scientific Data Visualization Using Python
Mayavi notebook This notebook contains python code examples for visualization using mlab module of Mayavi. The code samples are derived from the SciPy workshop conducted by Prof. Prabhu Ramachandran — available online on YouTube Clearing the figure window Parametrize a helix To get mgrid coordinates with linspace-like syntax Using the mesh command used for irregular…
Machine Learning Model Using Decision Trees — Random Forest Model
The use of Machine Learning (ML) algorithms in the field of big data analysis has been a thing of paramount importance in the current information age. In the context of scientific computing, ML models implementing these algorithms serve as a faster means to get to the results than pure iterations. In this article, we will…
Overview of Numerical Methods: The Backbone of Scientific Computing
Different numerical methods are the cornerstone of scientific computing, employed to solve the underlying models describing a physical problem. These numerical methods help solve the constitutive equations making up the mathematical framework. Numerical methods are often chosen according to their suitability and applicability based on the physical problem whose mathematical model counterpart it solves. At…
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Theoretical & Computational Materials Science 