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在人工智能时代,材料科学家的未来

Ayman Maqsood¹, Chen Chen¹, T Jesper Jacobsson^1,2

¹Institute of Photoelectronic Thin Film Devices and Technology, Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, Nankai University, Tianjin, 300350, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|March 13, 2024

概括

机器学习 (ML) 和人工智能 (AI) 加快了材料科学研究. 本文探讨了如何最好地整合这些技术,引入一个人工智能阶梯框架,以指导未来的发展和所需的技能.

科学领域:

材料科学材料科学材料科学
计算机科学计算机科学
人工智能的人工智能

背景情况:

材料科学的进步历史上与新的表征,合成和计算技术相关.
机器学习 (ML) 和人工智能 (AI) 是新兴技术,它们将对材料科学研究产生重大影响.
将ML/AI整合到材料科学研究中,在既定实践和所需技能方面,提供了机遇和挑战.

研究的目的:

探索将复杂的ML/AI方法集成到材料科学研究中.
确定人工智能时代对研究人员所需的新技能.
分析ML/AI对已建立的材料科学研究实践的潜在影响.

主要方法:

使用"人工智能梯子"比喻的概念框架开发.
机器学习/人工智能功能从基本数据装配到先进的自动化系统的层次分类.
讨论AI整合的机遇,挑战和不断发展的技能.

主要成果:

人工智能阶梯为了解材料科学中的ML/AI能力提供了一个结构化的方法.
识别了从数据拟合到复杂的人工智能功能 (如假设生成和自主实验) 的进展.
强调需要调整研究实践和开发新的技能.

结论:

机器学习和人工智能为加速材料发现和开发提供了一个强大的工具包.
人工智能阶梯框架有助于驾将人工智能整合到材料科学中的复杂性.
适应人工智能需要采取积极的方法来培养技能和研究方法的发展.

关键词:

人工智能的人工智能是人工智能.封闭循环实验的试验.机器学习是机器学习.材料科学是一种材料科学.

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