A mobile robotic chemist

By B. Burger, Phillip M. Maffettone, V. Gusev, Catherine M. Aitchison, Yang Bai, Xiao-yan Wang, Xiaobo Li, Ben M. Alston, Buyin Li, Rob Clowes and 4 more. Nature, 2020

1,244 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Networks and Communications, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/242bf67a2fda45bfc746fd30de0b17111f8d85e9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Mario Krenn

In Computer Networks and Communications

1. Alán Aspuru-Guzik
2. Shengding Hu
3. Maosong Sun
4. Weize Chen
5. Huadong Wang
6. Xin Cong
7. Yankai Lin
8. Cheng Yang

In Mechanical Engineering

1. Berend Smit

Citations in the corpus

• On scientific understanding with artificial intelligence (2022, background): “[65] Benjamin Burger, Phillip M Maffettone, Vladimir V Gusev, Catherine M Aitchison, Yang Bai, Xiaoyan Wang, Xiaobo Li, Ben M Alston, Buyi Li, Rob”
• Tool Learning with Foundation Models (2023, positive): “Apart from the software level, it is also possible for AI systems to manipulate practical platforms such as the synthetic robots (Burger et al., 2020), and to conduct synthetic experiments independently.”
• An autonomous laboratory for the accelerated synthesis of inorganic materials (2023, neutral): “Pioneering efforts have demonstrated autonomy in several aspects of materials research, including robotic and Bayesian-driven optimization of carbon nanotube yield 3,4 , photo-voltaic performance 5 and photocatalysis activity 6 .”
• The Role of Machine Learning in the Understanding and Design of Materials (2020, methodology): “The recent work of Burger et al. 137 introducing a mobile robotic chemist (Figure 7) demonstrates how fast this topic of research is growing, and it will be interesting to see whether it leads to the discovery of novel chemistries.”