Autonomous experimental systems in materials science
The emergence of autonomous experimental systems integrating machine learning and robots is ushering in a paradigm shift in materials science. Using computer algorithms and robots to decide and perform all experimental steps, these systems require no human intervention. A current direction focuses o...
Main Authors: | Naoya Ishizuki, Ryota Shimizu, Taro Hitosugi |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2023-12-01
|
Series: | Science and Technology of Advanced Materials: Methods |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/27660400.2023.2197519 |
Similar Items
-
Evolving intellectual property landscape for AI-driven innovations in the biomedical sector: opportunities in stable IP regime for shared success
by: Abhijit Poddar, et al.
Published: (2024-09-01) -
Human-in-the-loop transfer learning in collision avoidance of autonomous robots
by: Minako Oriyama, et al.
Published: (2025-03-01) -
How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data Science
by: Daniel Pacheco Gutierrez, et al.
Published: (2023-02-01) -
The Future of Material Scientists in an Age of Artificial Intelligence
by: Ayman Maqsood, et al.
Published: (2024-05-01) -
Autonomous experimental studies resistojet with two autonomous heating elements for nanosatellites
by: V. N. Blinov, et al.
Published: (2023-12-01)