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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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通过大型语言模型赋予通用主义物质智能权力.

Wenhao Yuan1, Guangyao Chen1,2, Zhilong Wang1,2

  • 1College of Engineering, Cornell University, Ithaca, NY, 14853, USA.

Advanced materials (Deerfield Beach, Fla.)
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大型语言模型 (LLM) 正在通过一个自主发现的循环中代理系统驱动通用材料智能 (GMI). 在LLM采用的挑战需要在材料科学公平实施的战略.

关键词:
自主代理人 独立代理人生成型的人工智能 (GAI)大型语言模型.物质情报物质情报是指物质情报.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 人工智能的人工智能
  • 计算科学 计算科学

背景情况:

  • 大型语言模型 (LLM) 正在成为材料科学的关键驱动力.
  • 通用材料智能 (GMI) 集成了推理,建模和验证.
  • 循环中的代理模式利用基于LLM的代理进行自主发现.

研究的目的:

  • 审查LLMs在材料科学中的变革性影响.
  • 讨论挑战并提出在GMI采用LLM的策略.
  • 概述一个路线图,以实施材料意识的LLMs.

主要方法:

  • 对材料科学中的LLM应用进行了全面的文献审查.
  • 在数据提取,属性预测,结构生成,合成规划和自动驾驶实验室中分析LLM角色.
  • 讨论挑战,包括知识不一致,资源需求和绩效不一致.

主要成果:

  • 法律学正在彻底改变传统的材料科学任务,并催化了代理在循环模式.
  • 在LLM课程中,我们将跨越本体学,概念学,计算学和实验学之间的差距.
  • 在扩大LLM采用方面存在重大挑战,原因是知识错位和资源密集性.

结论:

  • 在推进GMI方面,LLM是关键的,它使自主材料发现成为可能.
  • 解决LLM在适应性,效率和可信度方面的局限性至关重要.
  • 提出了一份战略路线图,以公平,民主地实施材料意识的LLMs.