Autonomous Discovery in the Chemical Sciences Part II: Outlook 论文

2019Angewandte Chemie International Edition引用 271
Machine Learning in Materials ScienceComputational Drug Discovery MethodsInnovative Microfluidic and Catalytic Techniques Innovation

详细信息

发表期刊/会议
Angewandte Chemie International Edition
发表日期
2019-09-25
发表年份
2019

关键词

Machine Learning in Materials ScienceComputational Drug Discovery MethodsInnovative Microfluidic and Catalytic Techniques Innovation

摘要

This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to "discover" despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.