计算机科学
领域(数学)
自治
加速度
工作(物理)
系统工程
数据科学
人机交互
人工智能
工程类
机械工程
经典力学
物理
法学
数学
纯数学
政治学
作者
Martha M. Flores‐Leonar,L.M. Mejía-Mendoza,Andrés Aguilar-Gránda,Benjamín Sánchez-Lengeling,Hermann Tribukait,Carlos Amador‐Bedolla,Alán Aspuru‐Guzik
标识
DOI:10.1016/j.cogsc.2020.100370
摘要
Materials Acceleration Platforms are an emerging paradigm to accelerate materials discovery as an effort to develop technology solutions that can help address or mitigate climate change concerns. These platforms combine artificial intelligence, robotic systems, and high-performance computing to achieve autonomous experimentation. Nevertheless, their development faces challenges to achieve full autonomy. In this work, we present state-of-the-art robotic platforms and machine learning approaches for autonomous experimentation, their integration, and applications, particularly in the field of materials for clean energy technologies. Later, we discuss the challenges and suggest improvements to be considered in the endeavor to accomplish autonomous experimentation.
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