Session-I/11h30m.md

-# Data-driven Materials Research with Experimental Research Data
+# Data platforms for Data-driven Materrials Science: ChemDX and MatDX
 
 | Information | Photo |
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-| `Name` <br> Hyunju Chang <br> <br> `Affiliation`<br> Chemical Data-driven Research Center, Korea Research Institute of Chemical Technology | <img src="uploads/176271cc821c4cf5e310d73c0078e936/image1.png" height="150"> |
+| `Name` <br> Jungho Shin <br> <br> `Affiliation`<br> Chemical Data-driven Research Center, Korea Research Institute of Chemical Technology | |
 
 ## Abstract
-**Jin-Soo Kim, Jin-Hoon Yang, Juhwan Noh, Gyoung S. Na, Yea-Lee Lee, Jungho Shin, Seunghun Jang, Jino Im and Hyunju Chang**
+**Jungho Shin, Yea-Lee Lee, Gyoung S. Na, , Seunghun Jang, Jino Im and Hyunju Chang**
 
 _Korea Research Institute of Chemical Technology (KRICT)_
 
-Recently, many data-driven studies have been reported in materials science. Many data-driven studies of materials mainly utilize databases from first-principles calculations or databases obtained from the literature. However, machine learning prediction models from computational data tend to be confined to the limitations of computational properties. Moreover, the data from the literature are limited in the champion data from the experiments. Building a machine-learning prediction model based on experimental research data, including the “dark results”, is necessary to develop new materials efficiently. In this presentation, I will introduce our recent works that collect and utilize experimental data from the entire material development cycle. First, we built web-based platforms to collect the research data easily for various applications, including thermoelectric materials, perovskite solar cell materials, etc. Then we built and utilized a machine-learning prediction model from the collected data to find new materials. As an example, I will discuss the following topics. <br><br> [1] SnSe-based thermoelectric material development <br> [2] Closed-loop optimization of catalyst for oxidative propane dehydrogenation with CO2
+In the field of materials science, the data-driven approach has played a crucial role in the discovery of novel materials over the past decade. This approach has been significantly accelerated by the emergence of valuable data infrastructures that adhere to FAIR (findable, accessible, interoperable, and reusable) data principles. Notable examples of these infrastructures include NOMAD, OPTIMADE, Materials Project, AFLOW, and OQMD. To effectively utilize these data infrastructures, it is necessary to integrate and classify various types of metadata related to materials properties. This integration and classification process enables the research community to make more accurate predictions using artificial intelligence techniques.
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+MatDX (Materials Data eXplorer) has been developed with a specific focus on the integration and classification of materials-related metadata based on materials ontologies. These ontologies encompass classes and instances related to material name, composition, compound, structure, property, and applications. MatDX utilizes a data warehouse solution to facilitate integration by connecting multiple databases. The introduction of material tags, derived from the materials ontologies, allows for easy and quick searching and retrieval of detailed information on materials of interest. Additionally, MatDX offers an interactive "Analysis" functionality that visually represents statistically significant relationships within the materials data.
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+The primary goal of these services is to enable researchers to discover new materials with desired properties based on a vast amount of research data. MatDX encompasses three sub-categories: PubDX, which focuses on published data; ExpDX, which pertains to experimental data; and CalcDX, which deals with calculated data. Researchers can access MatDX through the following web address: http://materials.chemdx.org.
 
 ## Biography
 
-> **Data-driven Materials Research with Experimental Research Data**
+> **Data platforms for Data-driven Materrials Science: ChemDX and MatDX**
 
-Dr. Hyunju Chang has worked as a research scientist at the Korea Research Institute of Chemical Technology (KRICT) in Korea since 1996. Now she is leading Chemical Data-driven Research Center at KRICT. She received B.S. and M.S. degrees in physic from Seoul National University (Korea) in 1986 and 1888, respectively, and a Ph.D. in condensed matter theory from Michigan State Univ. (USA ) in 1995. Before joining KRICT, she had worked as a post-doctor researcher at the dept. of Materials Sci. & Eng. of Northwestern Univ., USA (1995-1996). She has been working on computer-aided materials design for various materials, including oxides and nanomaterials. Her current research interests include materials informatics to utilize the materials data and to develop novel materials using machine learning from the materials database.
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