使用数据驱动学习来预测和控制无机材料合成的结果
Emily M Williamson1, Richard L Brutchey1
1Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.
Inorganic chemistry
|September 28, 2023
概括
数据驱动的方法,如实验设计 (DoE) 和机器学习,可以有效地控制无机材料合成. 这些方法可以实现可预测的结果,超越先进材料设计的传统试错方法.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算化学的计算化学
背景情况:
- 无机材料的合理设计对于各种应用至关重要.
- 传统的合成依赖于试错,这是低效的.
- 数据驱动技术提供了更可预测和可控制的方法.
研究的目的:
- 介绍关于在无机材料合成中使用数据驱动技术的观点.
- 为了比较实验设计 (DoE) 和用于合成控制的机器学习.
- 讨论实施这些方法的挑战和前景.
主要方法:
- 统计DoE和机器学习的比较分析.
- 审查最近的文学案例研究.
- 关于实验偏见和实施挑战的讨论.
主要成果:
- 设计选择 (DoE与机器学习) 影响控制,获得的信息和成本.
- 案例研究证明了数据驱动合成的有效性.
- 实验偏差可以影响结果.
结论:
- 数据驱动技术显著提高了无机材料合成中的可预测性和控制性.
- 广泛实施面临着需要解决的挑战.
- 未来的工作应该集中在克服这些障碍,以设计坚固的材料.
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