启发式
机器学习
计算机科学
人工智能
启发式
超启发式
创造力
数据科学
机器人学习
机器人
政治学
移动机器人
操作系统
法学
作者
Janine George,Geoffroy Hautier
标识
DOI:10.1016/j.trechm.2020.10.007
摘要
Chemical heuristics have been fundamental to the advancement of chemistry and materials science. These heuristics are typically established by scientists using knowledge and creativity to extract patterns from limited datasets. Machine learning offers opportunities to perfect this approach using computers and larger datasets. Here, we discuss the relationships between traditional heuristics and machine learning approaches. We show how traditional rules can be challenged by large-scale statistical assessment and how traditional concepts commonly used as features are feeding the machine learning techniques. We stress the waste involved in relearning chemical rules and the challenges in terms of data size requirements for purely data-driven approaches. Our view is that heuristic and machine learning approaches are at their best when they work together.
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