材料"经济学":使用机器学习结合化学,金融,环境和社会因素
Benjamin Poswell1, Amanda S Barnard1
1School of Computing, Australian National University, Acton ACT 2601, Australia.
ACS nano
|March 7, 2025
概括
先进的机器学习揭示了纳米材料性能与环境/社会影响之间的隐藏联系. 这种方法有助于实现碳中和未来的可持续发展.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算科学 计算科学
- 可持续性研究 可持续性研究
背景情况:
- 现代纳米材料对于可持续的未来至关重要,特别是在电池等储能领域.
- 当前的研究往往忽视了纳米材料开发的环境和社会经济后果.
- 研究和开发中的不透明因素阻碍了对可持续性的整体理解.
研究的目的:
- 应用先进的机器学习 (ML) 来揭示电化学性能和纳米材料的可持续性影响之间的潜在关系.
- 开发一个框架,使纳米材料研究和开发的决策更加合理,整体和可持续.
- 通过数据驱动的洞察力,识别阻碍可再生能源转型的障碍.
主要方法:
- 利用最先进的机器学习算法来分析复杂的数据集.
- 采用可解释的ML技术来揭示隐藏的模式和相关性.
- 使用公共电池组合数据集进行了案例研究,扩展了生命周期分析和关键性评估.
主要成果:
- 证明了可解释的ML能够识别材料特性和更广泛的影响之间的以前未知的联系.
- 提出了一个新的框架,将环境和社会经济因素整合到纳米材料评估中.
- 该框架增强了超越传统方法的可扩展性和解释能力.
结论:
- 先进的ML提供了一种强大的方法来理解纳米材料的多方面的影响.
- 这一框架有助于在现场做出更加知情和可持续的决策.
- 该方法可以帮助克服可再生能源转型的关键障碍,并推动可持续纳米材料研究.
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