Research

We use creativity and sophisticated computational methods for 

fundamental understanding and innovative design of materials.

Currently, CMAT@GIST is working on (1) fundamental understanding of materials properties, (2) machine-learning-driven design of new materials and (3) application of quantum computing algorithms to materials science.

1. Understanding materials

One of the main goals in materials science is to develop novel materials with enhanced properties. To this end, it is of utmost importance to achieve fundamental understanding on physics and chemistry of materials. Quantum mechanical calculations, primarily density functional theory (DFT) calculations, hold a special status as an inevitable tool for both understanding materials properties at an atomistic level and providing important insights into materials design. CMAT@GIST heavily uses DFT approach to achieve fundamental comprehension of unexplored materials and deliver valuable guidance for their experimental synthesis. 


2. Machine Learning: accelerated materials discovery

Designing new materials with improved properties takes enormous time and effort. This is because there are nearly infinite possibilities to form materials by combining elements from the periodic table. CMAT@GIST employs the state-of-the-art machine learning (ML) algorithms to rapidly explore the immense search space and aims to identify targeted materials with desired properties. Such ML-aided strategy will greatly  help researchers to develop new materials with enhanced performance.


3. Quantum Computing: next revolution

Currently, all computational algorithms are based on Boolean algebra which uses 0 and 1.  Often, such "classical" algorithms are not adequate in studying large and complex materials. Recently, there are active research efforts to develop quantum computers, which are working via "quantum" principles, to examine hard problems for classical computers. Since quantum computers are believed to be a game-changer in future, CMAT@GIST is working on applying quantum computing approaches to materials science.