材料信息学:人工智能时代的自主发现的出现
Turab Lookman1,2, YuJie Liu1, Zhibin Gao1
1State Key Laboratory for Mechanical Behavior of Materials, State Key Laboratory of Porous Metal Materials, School of Materials Science and Engineering, Xi'an Jiatong University, Xi'an, China.
Advanced materials (Deerfield Beach, Fla.)
|January 8, 2026
概括
材料信息学与机器学习和人工智能一起发展,加速了发现. 未来的人工智能集成有望实现自主材料科学,减少人类对研究的干预.
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
- 数据科学数据科学数据科学
背景情况:
- 材料信息学是从物理学和信息理论演变而来的.
- 机器学习和人工智能集成,特别是自2014-2016年以来,改变了材料发现.
- 美国材料基因组倡议推动了重大进展.
研究的目的:
- 为材料信息学的演变提供一个视角.
- 追踪该领域的历史背景,当前状态和未来方向.
- 审查关键的方法,并讨论挑战和解决方案.
主要方法:
- 历史贡献和基础思想的回顾.
- 分析机器学习和人工智能 (AI) 集成,包括深度学习和大型语言模型 (LLM).
- 讨论顺序设计方法,如贝叶斯优化和强化学习,以及变压器模型.
主要成果:
- 机器学习和人工智能,特别是LLM,现在是财产预测,合成规划和反向设计的组成部分.
- 自主自动驾驶实验室正在出现,由AI驱动.
- 确定了材料科学中LLM和贝叶斯优化常见的陷和解决方案.
结论:
- 人工智能正在将材料信息学从预测工具演变为合作伙伴.
- 积极学习和不确定性量化方面的进展为自主材料科学铺平了道路.
- 该领域正在朝着减少人类参与研究循环的方向发展.
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