Computational Materials Science

Computational materials research requires computational methods to solve interconnected problems with the materials. Specific mathematical models are available to examine issues on various length and time scales that help to explain the nature of material structures and how these structures efficiently regulate material properties. At the electronic point, Density Functional Theory (DFT) is a popular computational technique while atomic simulation methods Molecular Dynamics (MD) and Monte Carlo (MC) are considered preferred tools. Phase-field Process (PFM) is used routinely on micron and mesoscale (between micro and nano) regimes for materials problems.

•    Computational materials science and chemistry
•    Statistical/artificial intelligence methods, numerical techniques
•    Expert systems, genetic algorithms, neural networks
•    Process system design, engineering/materials/technological design
•    Mould flow analysis, modelling behavior of materials
•    Macroscopic/Mesoscopic Computational Materials Science
•    Nanomaterials synthesis, nano/micro manufacturing/technology
•    Electronic packaging technology, quality assessment, green manufacturing
•    Advanced manufacturing technology, machining, laser/electron beam treatment
•    Numerical simulation/techniques/algorithms
•    Computational physics/chemistry
•    3D printing, plastic deformation
•    Modelling of deformation behavior of materials
•    Materials/engineering databases

Market Analysis: Lightweight material markets are expected to grow at an 8.5 percent CAGR from USD 88.5 billion in 2014 to USD 133.1 billion in 2019. These lightweight materials make use of automotive, marine, and wind energy. The electroactive polymers market is expected to grow at a 7.9 percent CAGR from USD 3.26 billion in 2016 to USD 5.12 billion in 2022. Chemical industries in Asia and the Middle East are expected to grow by 3% on average over the next 20 years. For More Details:

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