Department of Materials Science and Engineering conducts researches to analyze and predict the structure and properties of materials using various analysis and simulation techniques.
Department of Materials Science and Engineering conducts researches to analyze and predict the structure and properties of materials using various analysis and simulation techniques. These include ultra-precise analysis tools that observe the structure of a material at atomic level using an electron microscopy, and a femtosecond laser-based ultra-fast analysis tools that observe the dynamic process of a material under an applied physical stimulus, and machine learning-based material design that develops new materials and optimizes physical properties using materials databases and machine learning platforms, and supercomputer-based multiscale simulations that accurately understand and predict materials properties and behaviors.
Since the unique property of the materials originates from its atoms and electrons, the understanding of material by visualizing the atoms and electronic structures is inevitable in the field of materials science. Furthermore, the atomic structural analysis can be applied to the materials design to create the unprecedented properties. Moreover, by using in-situ analysis technique, it has now become available to investigate the various dynamics by external factors such as heat, electric field/current, magnetic field, and mechanical force. Based on our ultra-precise materials analysis, we unveil structure-property correlation and further suggest new methods to develop novel materials with excellent properties for the next generation.
Femtosecond is 10-15 second, extremely short duration for which even light can only propagate 300 nanometer. However, an electron in materials collides with another electron in every 1 ~ 10 fs and collides with lattice in every 1,000 fs. If one can capture the moment that electron flows, absorbs or emits light, we can understand fundamental problems of materials in transistors, LED, and solar cells. Furthermore, the information can be utilized to develop completely new materials with advanced properties. Our department in POSTECH use ultrafast laser spectroscopy to directly capture the dynamics of electrons in materials.
Machine-learning is an imitation of human cognitive function. Machine-learning-based materials study refers to predicting material properties from the learned pattern using data in the same way that a researcher analyzes experimental results to find a specific rule. For example, Machine-learning can identify the part corresponding to a person’s face in the unknown picture by learning the regular arrangement of eyes, nose, mouth, ears, and head corresponds to a person’s face through many pictures. Likewise, machine-learning-based materials analysis is to predict materials properties by learning a database on materials properties with various atomic arrangements. Using these material databases and machine-learning platforms, we are conducting researches on designing new materials based on machine-learning technique that can accurately predict materials properties or quickly find materials with required properties.
Multiscale simulations are various computational techniques that understand and analyze complex materials phenomena that are difficult to reveal through experiments. Multiscale simulation techniques can identify materials behaviors at various time and length scales from individual atoms or electrons to devices used in real life by using various methods such as first-principles, molecular dynamics (MD), thermodynamics, phase field model (PFM), finite element method (FEM), etc. Recently, these techniques are widely used to efficiently develop and optimize new materials for various application areas at low cost by predicting the materials properties accurately.