project-machine learning-v2

Projects : Machine learning

Big-data collection

NLP based database generation from scientific document

 As the interest in machine learning increases, the interest in databases for use in machine learning is also increasing. Despite the increasing demand for databases in the material field, there is no clear database to date. Therefore, in this project, we intend to build a database based on research results in the field of materials existing in Internet documents through natural language processing techniques.

Machine learning application

Prediction of polymer properties using machine learning from the composition of various raw materials

  Various properties of copolymer generally come from their constituent units (monomer). Several factors including type of units, composition of units, manufacturing conditions can affect properties of copolymer. Traditionally, to achieve desired properties, trial-and-error experiment method have been used but the cost is high because a lot of samples to be prepared to test. In this project, to address the limitations, we predict properties of copolymer through the machine learning models. Experimental dataset comprised of over 350 features is used for training data. Based on the models, we aim to find out suitable materials and their optimized compositions for desired products.

AI based supercritical materials research

  As global issues emerge, exploration and development of resources and energy in extreme environments such as space, deep sea, and polar regions are actively progressing. Supercritical materials that greatly exceed the physical properties of existing materials are indispensable for stable work. However, its slow development speed and high probability of failure are blocked the progressing. Therefore, in this project, 7 promising supercritical materials that are used in the future extreme environment were selected, and an AI platform that integrates material design/manufacturing, synthesis/analysis, and evaluation is built to develop those materials in a relatively short time.

Development of Novel Resistance Switching Materials by using First Principle-based Machine Learning Platform

 AIM-HS(Ab Initio calculation based Machine learning High-throughput Screening) platform is Ab initio calculation high-throughput screening system based on multi-dimensional machine learning analysis. This project is purposed to detect hidden correlation between calculated properties and make criterion based on big data of Ab initio calculation. The final goal is material designing based on needs for material property.

AI image processing

Development of machine learning model for predicting corrosion of high corrosion-resistant alloy plated steel

 Alloy plating is used to prevent corrosion of steel construction materials, but it has limited lifespan under harsh environments. Through a machine learning model developed from alloy plated steel corrosion images and experimental environment data, we predict lifespan of construction materials in real marine and soil environments.


Completed projects