.png){kind=icon}
Return
An object-oriented framework to enable workflow evolution across materials acceleration platforms
.png){kind=icon}
OA
AI
DOI：10.1016/j.matt.2022.08.017.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Progress in data-driven self-driving laboratories for solving mate-rials 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 the world that would be able to effectively evolve experimental workflows. We demonstrate the framework via three experimental case studies from disparate fields to illustrate the evolution of, and seamless integration between, workflows, pro-moting efficient resource utilization and collaboration. Moving for-ward, 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, con-nected, global community of MAPs working together to solve scientific grand challenges.
Keywords:
MATERIALS DISCOVERY
MACHINE
COMPUTATION
Journal
IF:
17.5
Papers:
2.5K
Citations:
1.8W
Organization
Cited Papers
ShareSaveAutomated microfluidic platform for systematic studies of colloidal perovskite nanocrystals: towards continuous nano-manufacturing.png)
LAB ON A CHIP
LAB ON A CHIPIF5.4
PREAI
ShareSaveShareSaveA nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanoparticlesNATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveShareSaveShareSaveAutonomous experimentation systems for materials development: A community perspectiveMATTEROAAI
MATTERIF17.5
OAAIShareSave
View more