材料发现,开发和优化的人工智能
Benediktus Madika1, Aditi Saha1, Chaeyul Kang1
1Department of Materials Science and Engineering, KAIST, Daejeon 34141, Korea.
ACS nano
|July 25, 2025
概括
人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 正在改变材料科学,加速发现,开发和优化. 这些人工智能方法使先进的材料设计和分析成为可能,克服了未来创新的当前挑战.
科学领域:
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
- 数据科学数据科学数据科学
背景情况:
- 人工智能 (AI),机器学习 (ML) 和深度学习 (DL) 是现代科学研究中越来越重要的工具.
- 材料科学通过整合计算方法和数据分析取得了重大进展.
研究的目的:
- 审查人工智能,ML和DL对材料科学的变革性影响,涵盖发现,开发和优化.
- 引入与材料信息学相关的基本AI/ML概念和先进的DL模型.
- 讨论人工智能驱动材料科学的挑战和未来方向.
主要方法:
- 在材料发现 (结构生成,属性预测,高通量选,计算设计) 中对AI/ML/DL应用的审查.
- 在材料开发 (特征化,自主实验) 和优化 (设计,流程) 中探索AI.
- 介绍监督,无监督,半监督和强化学习,以及RNN,CNN,GNN,生成模型和变压器.
主要成果:
- 人工智能方法已经彻底改变了材料的发现和开发,提高了设计和流程.
- 材料信息学主题包括结构-属性关系,描述符,QSPR和数据管理策略等.
- 目前面临的挑战包括数据质量,模型可解释性和数据共享标准化.
结论:
- 未来的工作重点是提高AI的稳定性,整合因果推理和基于物理的AI,并使用多式模式模型.
- 量子计算与人工智能的整合承诺更快,更准确的结果.
- 伦理框架对于负责任的人类-人工智能合作至关重要,解决偏见,透明度和问责制.
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