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# Materials Data Standards and the development in China
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# AI-Enabled Materials Structure-Property-Synthesizability Predictions
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| Information | Photo |
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| ------ | ------ |
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| `Name` <br> Haiqing YIN <br> <br> `Affiliation`<br> University of Science and Technology Beijing <br> | <img src="uploads/781b650c808bcf1bd4e221690b113386/Yin.png" height="150"> |
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| `Name` <br> Yousung Jung <br> <br> `Affiliation`<br> Department of Chemical and Biological Engineering, Seoul National University <br> | <img src="uploads/998d9ea36e301a7cb9a99192f3813ce8/jung.jpeg" height="150"> |
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## Abstract
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Haiqing Yin<sup>a</sup>, Bin Xu<sup>a</sup>, Xue Jiang<sup>a</sup>, Cong Zhang<sup>a</sup>, Ruijie Zhang<sup>a</sup>, Yongwei Wang<sup>a</sup>, Shuichi Iwata<sup>b</sup>, Xuanhui Qu<sup>a</sup>
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<sup>a</sup> University of Science and Technology Beijing,100083, China <br>
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<sup>b</sup> The University of Tokyo, Tokyo,113-8654, Japan
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The FAIR principles advocate for the findability, accessibility, interoperability, and reuse of digital assets. Data standards are one of the key elements for the data curation in the big data e-commerce and e-government. approaches to tackle the challenging issue of data sharing and integration. The use of standardized data formats (data standards) supports the curation and archiving, data sharing, databases integrated with software tools, and cross-database retrieval. Data standardization provides a structure for creating and maintaining data quality as well. Currently Mmaterials data standards are especially urgently necessary for machine learning and AI for novel materials design and production processing optimization via machine learning and artificial intelligence in an era of digital trans formation. In this text, materials data standardization activites bloom in China when materials gonome engineering related researches were financially supported from the national and the local in recent years. Now the effort on building the materials data stanadards in China is extensively discussed, covering the data standards system, general rules for data from the various kinds of data sources and data description for computation, high throughput experimentation, as well as the specific materials data systems. And the data standards towards the international collaboration and the data ecosystem in the future will be also proposed to reach the goal of capable to find, access, communicate and reuse data with none or minimal human intervention.
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Discovery of new molecules and materials with desired properties is a practical goal of chemical research. A promising way to significantly accelerate the latter process is to incorporate all available knowledge and data to plan the synthesis of the next materials. In this talk, I will present several directions to use informatics and machine learning to efficiently explore chemical space. I will first describe methods of machine learning for fast and reliable predictions of materials and molecular properties. With these tools in place for property evaluation, I will then present a few generative frameworks that we have recently developed to allow the inverse design of molecules and materials with optimal target properties, either in the compositional space or structural space. One general challenge in digital discovery is that many of the molecules and materials that are computationally designed are often discarded in the laboratories since they are not synthesizable. I will thus lastly spend some time to talk about the synthesizability of molecules and materials, either by predicting the synthesis pathways (retrosynthesis) or chemical reactivity. Several challenges and opportunities that lie ahead for further developments of accelerated chemical platform will be discussed.
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## Biography
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Haiqing YIN is professor in University of Science and Technology Beijing (USTB), China. Her research focuses on material cross-scale design, database, machine learning and advanced materials on nickel-based superalloys, high-entropy alloys and boride cermets and steels, with over 100 papers published and more than 10 patents issued. She got her bachelor and master degree in Xi’an Jiaotong University, and PhD in USTB.
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> "AI-Enabled Materials Structure-Property-Synthesizability Predictions"
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Currently she is board member of Asia Materials Data Committee (AMDC), scientific data expert of Standardization Technical Committee for National Science and Technology Platform, Member of the branch Committee of the Group Standards Committee of the Powder Metallurgy Industry Technology Innovation Strategic Alliance, Member of the Material Genome Engineering Data Committee and Powder Metallurgy Field Committee for China Society for Testing and Materials (CSTM), and deputy director of Beijing Key Laboratory of Materials Genome Engineering. She also works as the editor of Journal of Chinese Science and Technology Resource and Journal of Powder Metallurgy Industry China. |
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Yousung Jung is a Professor of Chemical and Biological Engineering at Seoul National University. His research background and interests involve quantum chemistry and machine learning to develop efficient methods for fast and accurate simulations of complex molecular and materials systems, and their applications towards the understanding of molecules and materials for new discovery. Some of his recent works include the use of data science and machine learning to understand the structure-property-synthesizability relations for molecules and materials and use the obtained knowledge for inverse design. He received his Ph.D. in Theoretical Chemistry from University of California, Berkeley, with Martin Head-Gordon. After a postdoctoral work at Caltech with Rudy Marcus, he joined the faculty at KAIST in 2009, and moved to Seoul National University in 2023. He received the Hanseong Science Award by Hanseong Son Jae Han Foundation, KAIST Technology Innovation Award, Pole Medal by Asia-Pacific Association of Theoretical and Computational Chemists, Korean Chemical Society Young Physical Chemist Award, and KCS-Wiley Young Chemist Award. |
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