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...

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Bibliographic Details
Main Authors: 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
Other Authors: School of Materials Science and Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Materials
MAP6: Development
Data Driven
Online Access:https://hdl.handle.net/10356/164443

Internet

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

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