Trends in Chemistry ( IF 15.7 ) Pub Date : 2020-11-09 , DOI: 10.1016/j.trechm.2020.10.007 Janine George , Geoffroy Hautier
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.
中文翻译:
化学家与机器:传统知识与机器学习技术
化学启发法是化学和材料科学发展的基础。这些启发式方法通常是由科学家利用知识和创造力从有限的数据集中提取模式而建立的。机器学习为使用计算机和更大的数据集完善这种方法提供了机会。在这里,我们讨论传统启发式方法和机器学习方法之间的关系。我们展示了如何通过大规模统计评估来挑战传统规则,以及通常用作功能的传统概念如何为机器学习技术提供支持。我们强调重新学习化学规则所涉及的浪费以及纯数据驱动方法的数据大小要求方面的挑战。