斯皮尔斯纪念讲座:如何对化学和材料科学的人工智能进行有影响力的研究
Austin H Cheng1,2,3, Cher Tian Ser1,2,3, Marta Skreta2,3
1Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada. alan@aspuru.com.
Faraday discussions
|October 14, 2024
概括
机器学习正在改变化学和材料科学,但它的全部潜力仍未得到充分利用. 这一观点探讨了当前的应用,并提供了最大限度地提高机器学习的策略.
科学领域:
- 机器学习在化学和材料科学中的应用.
- 在计算和化学科学交叉的跨学科研究.
背景情况:
- 机器学习 (ML) 越来越多地融入科学研究.
- 尽管对化学和材料科学产生了重大影响,但ML尚未完全成熟.
- 目前的ML采用面临着实现其全部潜力的挑战.
研究的目的:
- 在各种化学问题中概述当前的机器学习应用.
- 讨论机器学习化学研究人员的观点和方法.
- 为增强化学ML研究影响提供考虑.
主要方法:
- 审查当前的机器学习应用在化学.
- 分析机器学习研究人员的方法和观点.
- 制定未来研究的战略考虑.
主要成果:
- 识别各种化学领域的广泛的当前ML应用.
- 了解ML研究人员如何处理化学问题.
- 优化ML对化学的贡献的关键考虑.
结论:
- 机器学习为推进化学和材料科学提供了巨大的机会.
- 更深入地了解ML方法和战略研究规划至关重要.
- 为了最大限度地提高ML的影响力,需要集中精力进行整合和方法论.
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