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
随着科学文献数量的快速增长和研究领域的不断深化,科研人员在提出创新性科学假设时面临巨大的信息处理挑战.尽管大语言模型(large language models,LLMs)在数据处理和知识整合方面展现出巨大潜力,但它们在生成具有创新性和深度的科学...随着科学文献数量的快速增长和研究领域的不断深化,科研人员在提出创新性科学假设时面临巨大的信息处理挑战.尽管大语言模型(large language models,LLMs)在数据处理和知识整合方面展现出巨大潜力,但它们在生成具有创新性和深度的科学假设方面仍存在许多不足.目前的研究主要集中在如何利用LLMs加速已有理论和技术的推进和完善,而忽视了科学研究从无到有的初始阶段,这一阶段涉及新假设的提出和新理论的构建,是科学进步的关键.基于结构智力理论中的发散思维和收敛思维,提出了一种创新的人机协作多智能体框架(human-in-the-loop multi-agent framework,HILMA),以实现可靠的初始科学假设生成.该框架结合实时系统化的知识检索增强机制,通过动态整合最新科研进展,构建引文网络子图,为LLMs提供前沿和完备的科研知识综述.同时,通过多智能体辩论方法模拟科学同行评审过程,并且结合人类专家的直觉和专业知识,进一步优化和精炼生成的假设,增强科学假设的多样性和论证深度.一系列人机评估表明,与现有基线相比,HILMA在生成高质量科学假设方面展现出显著优势,有望成为推动科技创新的关键工具.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.
文摘随着科学文献数量的快速增长和研究领域的不断深化,科研人员在提出创新性科学假设时面临巨大的信息处理挑战.尽管大语言模型(large language models,LLMs)在数据处理和知识整合方面展现出巨大潜力,但它们在生成具有创新性和深度的科学假设方面仍存在许多不足.目前的研究主要集中在如何利用LLMs加速已有理论和技术的推进和完善,而忽视了科学研究从无到有的初始阶段,这一阶段涉及新假设的提出和新理论的构建,是科学进步的关键.基于结构智力理论中的发散思维和收敛思维,提出了一种创新的人机协作多智能体框架(human-in-the-loop multi-agent framework,HILMA),以实现可靠的初始科学假设生成.该框架结合实时系统化的知识检索增强机制,通过动态整合最新科研进展,构建引文网络子图,为LLMs提供前沿和完备的科研知识综述.同时,通过多智能体辩论方法模拟科学同行评审过程,并且结合人类专家的直觉和专业知识,进一步优化和精炼生成的假设,增强科学假设的多样性和论证深度.一系列人机评估表明,与现有基线相比,HILMA在生成高质量科学假设方面展现出显著优势,有望成为推动科技创新的关键工具.
