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

• المؤلفون الرئيسيون: 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
• مؤلفون آخرون: School of Materials Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Materials; MAP6: Development; Data Driven
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/164443

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...