High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and ...High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and process optimization,characterized by low efficiency and high costs.The integration of Artificial Intelligence(AI)technologies has provided innovative solutions for HEAs research.This review presented a detailed overview of recent advancements in AI applications for structural modeling and mechanical property prediction of HEAs.Furthermore,it discussed the advantages of big data analytics in facilitating alloy composition design and screening,quality control,and defect prediction,as well as the construction and sharing of specialized material databases.The paper also addressed the existing challenges in current AI-driven HEAs research,including issues related to data quality,model interpretability,and cross-domain knowledge integration.Additionally,it proposed prospects for the synergistic development of AI-enhanced computational materials science and experimental validation systems.展开更多
An approach was proposed to specify the C4ISR capability of domain-specific modeling language.To confine the domain modeling within a standard architecture framework,formally a C4ISR capability meta-ontology was defin...An approach was proposed to specify the C4ISR capability of domain-specific modeling language.To confine the domain modeling within a standard architecture framework,formally a C4ISR capability meta-ontology was defined according to the meta-model of DoD Architecture Framework.The meta-ontology is used for extending UML Profile so that the domain experts can model the C4ISR domains using the C4ISR capability meta-concepts to define a domain-specific modeling language.The domain models can be then checked to guarantee the consistency and completeness through converting the UML models into the Description Logic ontology and making use of inference engine Pellet to verify the ontology.展开更多
文摘High-Entropy Alloys(HEAs)exhibit significant potential across multiple domains due to their unique properties.However,conventional research methodologies face limitations in composition design,property prediction,and process optimization,characterized by low efficiency and high costs.The integration of Artificial Intelligence(AI)technologies has provided innovative solutions for HEAs research.This review presented a detailed overview of recent advancements in AI applications for structural modeling and mechanical property prediction of HEAs.Furthermore,it discussed the advantages of big data analytics in facilitating alloy composition design and screening,quality control,and defect prediction,as well as the construction and sharing of specialized material databases.The paper also addressed the existing challenges in current AI-driven HEAs research,including issues related to data quality,model interpretability,and cross-domain knowledge integration.Additionally,it proposed prospects for the synergistic development of AI-enhanced computational materials science and experimental validation systems.
基金Project(2007AA01Z126) supported by the National High Technology Research and Development Program of ChinaProject(51306010202) supported by the National Defense Advance Research Program of China
文摘An approach was proposed to specify the C4ISR capability of domain-specific modeling language.To confine the domain modeling within a standard architecture framework,formally a C4ISR capability meta-ontology was defined according to the meta-model of DoD Architecture Framework.The meta-ontology is used for extending UML Profile so that the domain experts can model the C4ISR domains using the C4ISR capability meta-concepts to define a domain-specific modeling language.The domain models can be then checked to guarantee the consistency and completeness through converting the UML models into the Description Logic ontology and making use of inference engine Pellet to verify the ontology.
