Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164443
Title: An object-oriented framework to enable workflow evolution across materials acceleration platforms
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
Keywords: Engineering::Materials
Issue Date: 2022
Source: Leong, C. J., Low, A. K. Y., Recatala-Gomez, J., Velasco, P. Q., Vissol-Gaudin, E., Tan, J. D., Ramalingam, B., Made, R. I., Pethe, S. D., Sebastian, S., Lim, Y., Khoo, J. Z. H., Bai, Y., Cheng, J. J. W. & Hippalgaonkar, K. (2022). An object-oriented framework to enable workflow evolution across materials acceleration platforms. Matter, 5(10), 3124-3134. https://dx.doi.org/10.1016/j.matt.2022.08.017
Project: A1898b0043
NRF-NRFF13-2021-0011
Journal: Matter
Abstract: 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.
URI: https://hdl.handle.net/10356/164443
ISSN: 2590-2385
DOI: 10.1016/j.matt.2022.08.017
Rights: © 2022 Elsevier Inc. All rights reserved. This paper was published in Matter and is made available with permission of Elsevier Inc.
Fulltext Permission: embargo_20230831
Fulltext Availability: With Fulltext
Appears in Collections:MSE Journal Articles

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