在材料发现的不确定性下,以知识为导向的学习,优化和实验设计
Xiaoning Qian1,2, Byung-Jun Yoon1,2, Raymundo Arróyave3
1Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Patterns (New York, N.Y.)
|November 30, 2023
概括
通过从试错转向知识驱动的信息学,加速新型功能材料的发现. 本综述强调了贝叶斯信号处理和机器学习,以实现高效的,基于物理的研究和开发.
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:
- 目前的材料发现严重依赖于低效的试错方法和高通量选.
- 需要先进的信息技术来加快新型功能材料的发现.
研究的目的:
- 讨论转变材料发现实践中的关键研究问题和挑战.
- 突出基于知识的信息学的潜力,特别是贝叶斯信号处理和机器学习.
主要方法:
- 专注于不确定性意识和物理信息的贝叶斯信号处理和机器学习方案.
- 应用这些方法进行知识驱动的学习和强大的优化.
- 利用这些技术来实现高效,以目标为导向的实验设计.
主要成果:
- 确定了用于材料发现的先进信息学的重大研究问题和挑战.
- 展示了不确定性意识和物理知情人工智能的实用性,以加速研究.
- 提出了一个更有效,更有针对性的实验设计的框架.
结论:
- 向知识驱动的信息学进行根本性的转变对于加速功能性材料的发现至关重要.
- 贝叶斯信号处理和机器学习为这种转换提供了强大的工具.
- 将人工智能与领域知识相结合,可以实现更高效,更强大的材料研发.
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