Analytical·Computational Materials Science & Structural Materials Technology
Structural & Materials Characterization Technology
Structural material technology: We develop structural materials for excellent and reliable mechanical properties by microstructure design using experiments and artificial intelligence.
Materials Characterization and Simulation technology: We conduct researches to analyze and predict the structure and properties of materials using various analysis and simulation techniques.
STRUCTURAL MATERIAL: We develop structural materials for excellent and reliable mechanical properties by microstructure design using experiments and artificial intelligence. In addition to steel, Al, Mg, Ti, and novel high entropy alloys, manufacturing using 3D printing process is investigated for automotive, aviation/space, defense, biomaterials, electronics, energy industries.
CHARACTERIZATION AND SIMULATION: We conduct researches to analyze and predict the structure and properties of materials using various analysis and simulation techniques.
High Entropy Alloy
Since the early bronze age, humans have been tuning the properties of materials by adding alloying elements. With few exceptions, the basic alloying strategy of adding relatively small amounts of secondary elements to a primary element has remained unchanged over millennia. This means that composition adjustment of metallic alloys has long been used to lend desirable properties to materials. For the past decade and a half, however, a new alloying strategy that involves the combination of multiple principal elements in high concentrations to create new materials called high-entropy alloys has been emerging. High-entropy alloys have greatly expanded the compositional space for alloy design. The multidimensional compositional space that can be tackled with this approach is practically limitless, and only very small compositional regions have been investigated so far. Nevertheless, a few high-entropy alloys have already been shown to possess good mechanical, magnetic and invar properties, exceeding in part those of some conventional alloys, and more such high-entropy alloys are likely to be discovered in the future.
Metal Additive Manufacturing(3D Printing)
Additive Manufacturing (AM), or Metal 3D Printing, is a revolutionary metallurgical production method capable of producing highly complex parts directly from a computer file and raw material. Its high potential lies in its ability to manufacture customized products with individualization, complexity and weight reduction for free. The advantage of the additive manufacturing method, which allows almost unlimited object geometries to be produced, means that complex internal structures, which is why corresponding processes are used today in toolmaking and medical technology.
Highly individualized mass goods therefore require manufacturing processes that are cost-effective and close to the final contour, i.e. without expensive post-processing steps. Additive manufacturing fulfils such criteria and enables individual or small series production with almost unlimited creative freedom.
Electrochemical corrosion and anti-corrosion
In modern times, with the development of stainless steel, the corrosion resistance of steel has improved dramatically. Corrosion in extreme environments such as cryogenic, high temperatures, abrasion and seawater environments still causes great economic and environmental damage industrially and acts as a cause of safety problems. Since it is extremely difficult or impossible to replace and repair parts in such a harsh environment, the need for structural materials with strong corrosion resistance is increasing day by day. Among them, high entropy alloys are in the spotlight as structural materials that can serve in harsh environments due to their characteristics that differ from the general predictions applied to conventional metal materials. In particular, the CoCrMnFeNi alloy represented by cantor alloy has been reported several times that it has superior corrosion resistance properties than the typical stainless steel Type 304L. In our department, studies are being actively conducted to analyze the corrosion behavior of various types of high-strength structural materials and to protect them.
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ultra-precise materials analysis
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.
ultra-fast materials analysis
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-based materials analysis
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
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.
