Session-I/11h30m.md

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 | `Name` <br> Hyunju Chang <br> `Affiliation`<br> Chemical Data-driven Research Center, Korea Research Institute of Chemical Technology <br> `E-mail` <br> hjchang@krict.re.kr | <img src="uploads/176271cc821c4cf5e310d73c0078e936/image1.png" height="150"> |
 
 ## Abstract
+Jin-Soo Kim, Jin-Hoon Yang, Juhwan Noh, Gyoung S. Na, Yea-Lee Lee, Jungho Shin, 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