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...

Full description

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:
Online Access:https://hdl.handle.net/10356/164443
Description
Summary: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 acceleration platforms (MAPs), we propose a unified framework to enable collaboration between MAPs, leveraging on object-oriented programming principles using research groups around theworldthatwouldbeabletoeffectively evolveexperimentalworkflows.Wedemonstratetheframeworkvia three experimental case studies from disparate fields to illustrate theevolutionof,andseamlessintegrationbetween,workflows,promoting efficient resource utilization and collaboration. Moving forward, we project our framework on three other research areas that would benefit from such an evolving workflow. Through the wide adoption of our framework, we envision a collaborative, connected, global community of MAPs working together to solve scientific grand challenges.