{"666796":{"#nid":"666796","#data":{"type":"news","title":"New Research Explores Using Generative AI Technology for Materials Discovery","body":[{"value":"\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EWith the explosive rise of popular artificial intelligence applications like ChatGPT and DALL-E, consumers are becoming more and more familiar with the world of generative models. While these fun, novel tools are helpful in our everyday lives, Georgia Tech researchers are using the same technology to make new scientific discoveries and solve complex engineering challenges.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EOne example of this is\u0026nbsp;\u003Cstrong\u003EVictor Fung\u003C\/strong\u003E, an assistant professor with Georgia Tech\u2019s School of Computational Science and Engineering (CSE). Fung recently led a research team that\u0026nbsp;\u003Ca href=\u0022https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/aca1f7\u0022\u003Edeveloped a new, first-of-its-kind algorithm\u003C\/a\u003E\u0026nbsp;that can reconstruct atomic structure in generative models.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EA significant application Fung focuses this research toward is in the field of materials science and engineering. The algorithm could be key in developing further AI tools and new materials to the benefit of individual researchers and entire communities alike.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cStructural representations are a well-known concept people have used in other machine learning applications for chemistry and materials, like training models to predict energies and forces,\u201d Fung said. \u201cBut this is really the first time that anyone has used this in generative models.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EStructure is a key property in a material design. For example, structure plays a role in determining superconductivity within electronics, biological viability in drugs, and catalyzation of certain chemical reactions.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EFung explained that using generative models to study atomic structure, and to design new materials, could be vital in climate remediation. This may include developing greener catalysts for use in fuel cells, designing better material for carbon capture, and discovering new light-absorbent molecules for application in solar panels.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EThe algorithm can help engineers create new materials with targeted properties by building models atom-by-atom, a concept called inverse design. The algorithm is a progressive step forward in allowing computer models to create new materials tailor-made with specific functions and characteristics in mind by designers.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ESpecifically, the algorithm allows materials scientists to know the exact structure of materials that exhibit a desired property, potentially making proposed material designs a reality.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cIf we know the structure of material, we can be sure of what properties it has, and we will have a clear goal to try to synthesize it and develop applications,\u201d Fung said. \u201cWe basically have the key to defining the material in the chemical space.\u201d\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EFung\u2019s paper is the first in a forthcoming series of studies to develop new generative models for atomic structure. He and his co-researchers think the series could result in new algorithms and models that yield commercial benefits, as well as solve large, scientific problems.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAs part of this campaign to share his research,\u0026nbsp;\u003C\/span\u003EFung is set to discuss the findings March 31 at\u0026nbsp;\u003Ca href=\u0022https:\/\/research.gatech.edu\/materials\/imatsymposium\u0022\u003E2023 Symposium on Materials Innovations\u003C\/a\u003E, hosted by Georgia Tech\u2019s Institute for Materials (IMat).\u0026nbsp;\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ESchool of CSE Ph.D. student\u0026nbsp;\u003Cstrong\u003EShuyi Jia\u003C\/strong\u003E\u0026nbsp;worked with Fung to develop the algorithm and is a co-author on the paper. The pair partnered with Oak Ridge National Laboratory scientists\u0026nbsp;\u003Cstrong\u003EJiaxin Zhang\u003C\/strong\u003E,\u0026nbsp;\u003Cstrong\u003EJunqi Yin\u003C\/strong\u003E, and\u0026nbsp;\u003Cstrong\u003EPanchapakesan Ganesh\u003C\/strong\u003E\u0026nbsp;through the study.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EAlong with AI tools like ChatGPT and DALL-E, generative models are popularly used today in images, text, audio, and other types of information. They are not as common in overcoming scientific challenges due to their data-intensive nature, an obstacle that Fung\u2019s algorithm helps overcome.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EIn technical terms, the algorithm makes it possible for generative models to work with non-invertible structural representations, such as atom-centered symmetry functions.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003ENow that the group has learned how to use models to generate structure, they want to extend this to broader problems in materials design and discovery. This includes being able to generate structures with different chemical compositions as well.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EHere, their algorithm becomes a tested, verified method using generative models to understand and overcome complex engineering problems.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u201cPeople who are interested in solving these kinds of problems in materials discovery, whether for specific applications, specific types of materials, or specific properties, can potentially use this approach, or at least take inspiration from it,\u201d Fung said.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","summary":"","format":"basic_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ESchool of Computational Science and Engineering Assistant Professor is presenting details about a first-of-its-kind algorithm for generative AI models at the\u0026nbsp;\u003Ca href=\u0022https:\/\/research.gatech.edu\/materials\/imatsymposium\u0022\u003E2023 Symposium on Materials Innovations\u003C\/a\u003E, being hosted by Georgia Tech\u2019s Institute for Materials on March 31.\u003C\/p\u003E\r\n","format":"basic_html"}],"field_summary_sentence":[{"value":"Applications of a novel algorithm developed by School of CSE researchers may lead to the design of new climate-remediation materials."}],"uid":"32045","created_gmt":"2023-03-24 13:32:29","changed_gmt":"2023-04-06 15:03:28","author":"Ben Snedeker","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2023-03-24T00:00:00-04:00","iso_date":"2023-03-24T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"670465":{"id":"670465","type":"image","title":"Victor Fung CRNCH.jpeg","body":"\u003Cp\u003E\u003Cstrong\u003EVictor Fung\u003C\/strong\u003E, an assistant professor with Georgia Tech\u2019s School of Computational Science and Engineering, speaks during a panel discussion during a workshop on campus.\u003C\/p\u003E\r\n","created":"1680793152","gmt_created":"2023-04-06 14:59:12","changed":"1680793152","gmt_changed":"2023-04-06 14:59:12","alt":"Victor Fung, an assistant professor with Georgia Tech\u2019s School of Computational Science and Engineering","file":{"fid":"253324","name":"Victor Fung CRNCH.jpeg","image_path":"\/sites\/default\/files\/2023\/04\/06\/Victor%20Fung%20CRNCH.jpeg","image_full_path":"http:\/\/tlwarc.hg.gatech.edu\/\/sites\/default\/files\/2023\/04\/06\/Victor%20Fung%20CRNCH.jpeg","mime":"image\/jpeg","size":35631,"path_740":"http:\/\/tlwarc.hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2023\/04\/06\/Victor%20Fung%20CRNCH.jpeg?itok=7fESeusd"}}},"media_ids":["670465"],"groups":[{"id":"576481","name":"ML@GT"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[{"id":"134","name":"Student and Faculty"},{"id":"153","name":"Computer Science\/Information Technology and Security"},{"id":"154","name":"Environment"}],"keywords":[{"id":"192390","name":"generative AI"},{"id":"187812","name":"artificial intelligence (AI)"},{"id":"84281","name":"advanced materials"}],"core_research_areas":[{"id":"39471","name":"Materials"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EBryant Wine, Communications Officer I\u003Cbr \/\u003E\r\n\u003Ca href=\u0022bryant.wine@cc.gatech.edu\u0022\u003Ebryant.wine@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E\r\n","format":"basic_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}