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

• 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: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Materials; MAP6: Development; Data Driven
• Online Access: https://hdl.handle.net/10356/164443
• Institution: Nanyang Technological University

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...