追求异常:机器学习在化学和材料科学方面的研究方向
Joshua Schrier1, Alexander J Norquist2, Tonio Buonassisi3
1Department of Chemistry, Fordham University, The Bronx, New York 10458, United States.
Journal of the American Chemical Society
|September 27, 2023
概括
机器学习 (ML) 可以找到特殊的材料,但目前的方法优化了现有的材料. 需要新的ML方法来发现真正的新分子和材料,
科学领域:
- 材料科学
- 计算化学
- 机器学习
背景情况:
- 特殊的分子和材料提供了技术价值和基本见解,
- 历史上,发现依赖于偶然性,但机器学习 (ML) 和自动化现在被提议加速识别和合成.
- 目前的数据驱动的机器学习方法在优化方面是卓越的,
研究的目的:
- 认为传统的ML方法不足以发现新的特殊材料.
- 建议将ML方法转向识别异常值,而不是优化现有属性.
- 为开发能够发现特殊材料的ML方法提供可操作的建议.
主要方法:
- 分析涉及高温 (高Tc) 氧化物超导体和超硬材料的案例研究.
- 在材料发现的背景下批评传统的ML方法.
- 制定六项推,以推动特殊材料的ML驱动的发现.
主要成果:
- 展示使用氧化物超导体和超硬材料的ML引导发现的挑战.
- 确定目前用于发现特殊材料的自动化限制.
- 为开发有效的ML方法发现异常值提出六个关键原则.
结论:
- 传统的ML方法更适合优化已知的材料特性,而不是发现新的特殊材料.
- 需要对机器学习进行转变,
- 将建议纳入自动化工作流程可以发现突破性的分子和材料.
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