Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence (AI). AI has successfully been applied for predicting the formability, revealing the properties, and guiding the experimental synthesis of materials. Rapid progress has been made in the integration of increasing database and improved computing power. Though some reviews present the development from their unique aspects, reviews from the view of how AI empowered both discovery of new materials and cognition of existing materials that covers the completed contents with two synergistical aspects are few. Here, the newest development is systematically reviewed in the field of AI empowered materials, reflecting advanced design of the intelligent systems for discovery, synthesis, prediction and validation of materials. First, background and mechanisms are briefed, after which the design for the AI systems with data, machine learning and automated laboratory included is illustrated. Next, strategies are summarized to obtain the AI systems for materials with improved performance which comprehensively cover the aspects from the in-depth cognizance of existing material and the rapid discovery of new materials, and then, the design thought for future AI systems in material science is pointed out. Finally, some perspectives are put forward.
{"title":"Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation.","authors":"Ying Cao,Hong Fu,Jian Lu,Yuejiao Chen,Titao Jing,Xi Fan,Bingang Xu","doi":"10.1007/s40820-025-01945-4","DOIUrl":"https://doi.org/10.1007/s40820-025-01945-4","url":null,"abstract":"Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence (AI). AI has successfully been applied for predicting the formability, revealing the properties, and guiding the experimental synthesis of materials. Rapid progress has been made in the integration of increasing database and improved computing power. Though some reviews present the development from their unique aspects, reviews from the view of how AI empowered both discovery of new materials and cognition of existing materials that covers the completed contents with two synergistical aspects are few. Here, the newest development is systematically reviewed in the field of AI empowered materials, reflecting advanced design of the intelligent systems for discovery, synthesis, prediction and validation of materials. First, background and mechanisms are briefed, after which the design for the AI systems with data, machine learning and automated laboratory included is illustrated. Next, strategies are summarized to obtain the AI systems for materials with improved performance which comprehensively cover the aspects from the in-depth cognizance of existing material and the rapid discovery of new materials, and then, the design thought for future AI systems in material science is pointed out. Finally, some perspectives are put forward.","PeriodicalId":714,"journal":{"name":"Nano-Micro Letters","volume":"84 1","pages":"109"},"PeriodicalIF":26.6,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145937632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wood, once regarded primarily as a structural material, possesses rich physicochemical complexity that has long been underexplored. In the context of industrialization and carbon imbalance, it is now emerging as a renewable and multifunctional platform for green nanotechnologies. Recent advances in wood nanotechnology have enabled the transformation of natural wood into programmable substrates with tailored nanoarchitectures, establishing it as a representative class of bio-based nanomaterials. This review systematically categorizes wood-specific nanoengineering strategies-including thermal carbonization, laser-induced graphenization, targeted delignification, nanomaterial integration, and mechanical processing-highlighting their mechanisms and impacts on wood's multiscale structural and functional properties. Importantly, these functionalization strategies can be flexibly combined in a modular, "Lego-like" manner, enabling wood to be reconfigured and optimized for diverse application scenarios. We summarize recent progress in applying functionalized wood to sustainable technologies such as energy storage (e.g., metal-ion batteries, Zn-air systems, supercapacitors), water treatment (e.g., adsorption, photothermal filtration, catalytic degradation), and energy conversion (e.g., solar evaporation, ionic thermoelectrics, hydrovoltaics, and triboelectric nanogenerators). These studies reveal how nanoengineered wood structures can enable efficient charge transport, selective adsorption, and enhanced light-to-heat conversion. Finally, the review discusses current challenges-such as scalable fabrication, material integration, and long-term environmental stability-and outlines future directions for the development of wood-based platforms in next-generation green energy and environmental systems.
{"title":"Functionalized Wood: A Green Nanoengineering Platform for Sustainable Technologies.","authors":"Tuo Zhang,Mingwei Gu,Yizhu Liu,Guangyao Chen,Haiyang Zhang,Liguo Chen,Xingwen Zhou,Lining Sun,Zhen Wen,Yunlei Zhou,Haibo Huang","doi":"10.1007/s40820-025-01953-4","DOIUrl":"https://doi.org/10.1007/s40820-025-01953-4","url":null,"abstract":"Wood, once regarded primarily as a structural material, possesses rich physicochemical complexity that has long been underexplored. In the context of industrialization and carbon imbalance, it is now emerging as a renewable and multifunctional platform for green nanotechnologies. Recent advances in wood nanotechnology have enabled the transformation of natural wood into programmable substrates with tailored nanoarchitectures, establishing it as a representative class of bio-based nanomaterials. This review systematically categorizes wood-specific nanoengineering strategies-including thermal carbonization, laser-induced graphenization, targeted delignification, nanomaterial integration, and mechanical processing-highlighting their mechanisms and impacts on wood's multiscale structural and functional properties. Importantly, these functionalization strategies can be flexibly combined in a modular, \"Lego-like\" manner, enabling wood to be reconfigured and optimized for diverse application scenarios. We summarize recent progress in applying functionalized wood to sustainable technologies such as energy storage (e.g., metal-ion batteries, Zn-air systems, supercapacitors), water treatment (e.g., adsorption, photothermal filtration, catalytic degradation), and energy conversion (e.g., solar evaporation, ionic thermoelectrics, hydrovoltaics, and triboelectric nanogenerators). These studies reveal how nanoengineered wood structures can enable efficient charge transport, selective adsorption, and enhanced light-to-heat conversion. Finally, the review discusses current challenges-such as scalable fabrication, material integration, and long-term environmental stability-and outlines future directions for the development of wood-based platforms in next-generation green energy and environmental systems.","PeriodicalId":714,"journal":{"name":"Nano-Micro Letters","volume":"57 1","pages":"108"},"PeriodicalIF":26.6,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145937633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}