Jaejung Park, Jongmok Lee, Jaejun Lee, Kyoungmin Min, Haesun Park, Seungchul Lee
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To address this challenge, this study proposes an active learning (AL) framework consisting of a surrogate model and utility function for expeditious identification of thermodynamically stable MXenes in the extensive chemical space of 23,857 MXenes with compositional and stoichiometric diversity. Exploiting the fast inference speed and the capability of the AL framework to accurately identify stable MXenes, only 480 DFT calculations were required to identify 126 thermodynamically stable MXenes; among these, the stabilities of 89 MXenes have not been previously reported. In contrast, only two stable MXenes were identified among randomly selected 1693 MXenes, demonstrating the inefficiency of using only DFT calculations in exploring a large chemical space. The AL framework successfully minimized the number of DFT calculations while maximizing that of thermodynamically stable MXenes identified and can contribute to future studies in finding stable MXenes expeditiously.","PeriodicalId":21,"journal":{"name":"ACS Nano","volume":null,"pages":null},"PeriodicalIF":15.8000,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Active Learning Framework for Expediting the Search of Thermodynamically Stable MXenes in the Extensive Chemical Space\",\"authors\":\"Jaejung Park, Jongmok Lee, Jaejun Lee, Kyoungmin Min, Haesun Park, Seungchul Lee\",\"doi\":\"10.1021/acsnano.4c08621\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"MXenes possess a wide range of materials properties owing to their compositional and stoichiometric diversities, facilitating their utilization in various technological applications such as electrodes, catalysts, and supercapacitors. To explore their applicability, identification of thermodynamically stable and synthesizable MXenes should precede. The energy above the convex hull (<i>E</i><sup>hull</sup>) calculated using the density functional theory (DFT) is a powerful scale to probe the thermodynamic stability. However, the high calculation cost of DFT limits the search space of unknown chemistry. To address this challenge, this study proposes an active learning (AL) framework consisting of a surrogate model and utility function for expeditious identification of thermodynamically stable MXenes in the extensive chemical space of 23,857 MXenes with compositional and stoichiometric diversity. Exploiting the fast inference speed and the capability of the AL framework to accurately identify stable MXenes, only 480 DFT calculations were required to identify 126 thermodynamically stable MXenes; among these, the stabilities of 89 MXenes have not been previously reported. In contrast, only two stable MXenes were identified among randomly selected 1693 MXenes, demonstrating the inefficiency of using only DFT calculations in exploring a large chemical space. The AL framework successfully minimized the number of DFT calculations while maximizing that of thermodynamically stable MXenes identified and can contribute to future studies in finding stable MXenes expeditiously.\",\"PeriodicalId\":21,\"journal\":{\"name\":\"ACS Nano\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":15.8000,\"publicationDate\":\"2024-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Nano\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1021/acsnano.4c08621\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Nano","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1021/acsnano.4c08621","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Active Learning Framework for Expediting the Search of Thermodynamically Stable MXenes in the Extensive Chemical Space
MXenes possess a wide range of materials properties owing to their compositional and stoichiometric diversities, facilitating their utilization in various technological applications such as electrodes, catalysts, and supercapacitors. To explore their applicability, identification of thermodynamically stable and synthesizable MXenes should precede. The energy above the convex hull (Ehull) calculated using the density functional theory (DFT) is a powerful scale to probe the thermodynamic stability. However, the high calculation cost of DFT limits the search space of unknown chemistry. To address this challenge, this study proposes an active learning (AL) framework consisting of a surrogate model and utility function for expeditious identification of thermodynamically stable MXenes in the extensive chemical space of 23,857 MXenes with compositional and stoichiometric diversity. Exploiting the fast inference speed and the capability of the AL framework to accurately identify stable MXenes, only 480 DFT calculations were required to identify 126 thermodynamically stable MXenes; among these, the stabilities of 89 MXenes have not been previously reported. In contrast, only two stable MXenes were identified among randomly selected 1693 MXenes, demonstrating the inefficiency of using only DFT calculations in exploring a large chemical space. The AL framework successfully minimized the number of DFT calculations while maximizing that of thermodynamically stable MXenes identified and can contribute to future studies in finding stable MXenes expeditiously.
期刊介绍:
ACS Nano, published monthly, serves as an international forum for comprehensive articles on nanoscience and nanotechnology research at the intersections of chemistry, biology, materials science, physics, and engineering. The journal fosters communication among scientists in these communities, facilitating collaboration, new research opportunities, and advancements through discoveries. ACS Nano covers synthesis, assembly, characterization, theory, and simulation of nanostructures, nanobiotechnology, nanofabrication, methods and tools for nanoscience and nanotechnology, and self- and directed-assembly. Alongside original research articles, it offers thorough reviews, perspectives on cutting-edge research, and discussions envisioning the future of nanoscience and nanotechnology.