{
    "abstract": "Discovering novel infrared functional materials (IRFMs) hold tremendous significance for laser industry. Incorporating artificial intelligence into material discovery has been recognized as a pivotal trend driving advancements in materials science. In this work, an IRFM predictor based on machine learning (ML) is developed for the pre-selection of the most promising candidates, in which interpretable analyses reveal the prior domain knowledge of IRFMs. Under the guidance of this IRFM predictor, a series of selenoborates, ABa",
    "authors": [
        {
            "affiliation": "Research Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.\nCenter of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.",
            "firstname": "Yihan",
            "initials": "Y",
            "lastname": "Yun"
        },
        {
            "affiliation": "Research Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.",
            "firstname": "Mengfan",
            "initials": "M",
            "lastname": "Wu"
        },
        {
            "affiliation": "Research Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.\nCenter of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.",
            "firstname": "Zhihua",
            "initials": "Z",
            "lastname": "Yang"
        },
        {
            "affiliation": "Research Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.\nCenter of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.",
            "firstname": "Guangmao",
            "initials": "G",
            "lastname": "Li"
        },
        {
            "affiliation": "Research Center for Crystal Materials; State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions; Xinjiang Key Laboratory of Functional Crystal Materials; Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 40-1 South Beijing Road, Urumqi, 830011, China.\nCenter of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.",
            "firstname": "Shilie",
            "initials": "S",
            "lastname": "Pan"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9 2025 The Author(s). Advanced Science published by Wiley\u2010VCH GmbH.",
    "doi": "10.1002/advs.202417851",
    "journal": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)",
    "keywords": [
        "chalcogenides",
        "data\u2010driven machine learning",
        "nonlinear optical materials",
        "selenoborates"
    ],
    "methods": null,
    "publication_date": "2025-04-26",
    "pubmed_id": "40285597",
    "results": null,
    "title": "Synergistic Machine Learning Guided Discovery of ABa",
    "xml": "<Element 'PubmedArticle' at 0x788504c61a80>"
}