在纳米科学及其他领域的机器学习:工作流程,数据处理,XAI和ITAP指标,基于语言的模型.
Junnan Song1, Qingjie Sun2, Andre G Skirtach1
1Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium.
Advances in colloid and interface science
|December 18, 2025
概括
人工智能 (AI) 和机器学习 (ML) 正在彻底改变纳米科学,从数据处理到人工智能辅助制造. 本综述探讨了人工智能模型及其在透明,可重复的纳米建筑学中的应用.
科学领域:
- *纳米科学,包括合物,接口和材料科学.
- * 将人工智能 (AI) 和机器学习 (ML) 整合到科学发现中.
背景情况:
- * 之前的工作 (律师) 合体接口科学. 2025,343,103546) 涵盖了纳米建筑学中的ML.
- * 本次审查将分析扩展到现代工作流程,数据处理和语言模型.
研究的目的:
- * 审查人工智能在体和界面科学中的应用.
- *探索数据处理,模型开发和人工智能辅助的材料科学.
主要方法:
- *围绕数据采集,模型开发 (浅层到深度学习) 和人工智能辅助制造进行组织.
- * 监督,无监督,半监督和强化学习模型的比较.
- *使用ITAP框架对可解释AI (XAI) 的研究.
主要成果:
- *人工智能模型应用于材料创新管道:设计,合成,属性预测和性能评估.
- *XAI框架提高了模型的透明度,可靠性和可解释性.
- *讨论自主实验室和人工智能驱动的纳米架构的未来方向.
结论:
- *人工智能和机器学习对于推进纳米建筑学至关重要.
- *预计语言模型将在未来的AI应用中发挥重要作用.
- *强调透明,可重复和可持续的AI驱动型研究.
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