通过研讨会为材料信息学教育提供框架
Arun Mannodi-Kanakkithodi1, Austin McDannald2, Shijing Sun3
1Materials Engineering, Purdue University, West Lafayette, USA.
概括
材料信息学教育对于培养未来科学家在数据科学,人工智能 (AI) 和机器学习 (ML) 方面至关重要. 实践研讨会有效地向研究人员介绍用于材料数据分析的AI/ML工具.
科学领域:
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
- 计算科学 计算科学
背景情况:
- 材料信息学领域需要熟练掌握数据科学,人工智能和ML的科学家.
- 传统的课程往往缺乏这些领域的综合培训.
研究的目的:
- 突出材料信息学教育的重要性.
- 为材料研究人员详细介绍AI/ML研讨会的结构和成功情况.
- 倡导将AI/ML培训纳入材料科学教育.
主要方法:
- 在2022年的MRS会议上,对材料数据的AI/ML进行了实践研讨会.
- 专注于基本的AI/ML概念,算法和实际应用.
- 利用竞赛来增强参与和学习.
主要成果:
- 成功地向材料研究人员介绍了AI/ML工具和概念.
- 证明了研讨会在促进AI/ML的实际应用方面的有效性.
- 收到了积极的反和未来车间整合的计划.
结论:
- 实践研讨会是材料科学家学习和应用AI/ML的有效方法.
- 在AI/ML的继续教育对于材料信息学的进步至关重要.
- 研讨会为传播AI/ML知识提供了一个可扩展和可访问的平台.
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