An object-oriented framework to enable workflow evolution across materials acceleration platforms

An object-oriented framework to enable workflow evolution across materials acceleration platforms

Progress in data-driven self-driving laboratories for solving materials grand challenges has accelerated with the advent of machine learning, robotics, and automation, but they are usually designed with specific materials and processes in mind. To develop the next generation of materials acceleratio...

Täydet tiedot

Bibliografiset tiedot
Päätekijät: Leong, Chang Jie, Low, Andre Kai Yuan, Recatala-Gomez, Jose, Velasco, Pablo Quijano, Vissol-Gaudin, Eleonore, Tan, Jin Da, Ramalingam, Balamurugan, Made, Riko I, Pethe, Shreyas Dinesh, Sebastian, Saumya, Lim, Yee-Fun, Khoo, Jonathan Zi Hui, Bai, Yang, Cheng, Jayce Jian Wei, Hippalgaonkar, Kedar
Muut tekijät: School of Materials Science and Engineering
Aineistotyyppi: Journal Article
Kieli:English
Julkaistu: 2023
Aiheet:
Engineering::Materials
MAP6: Development
Data Driven
Linkit:https://hdl.handle.net/10356/164443

Internet

https://hdl.handle.net/10356/164443

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