Language:

On-the-fly closed-loop materials discovery via Bayesian active learning

Nature communications, 2020-11, Vol.11 (1), p.5966-5966, Article 5966 [Peer Reviewed Journal]

This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2020 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-020-19597-w ;PMID: 33235197

Full text available

Citations Cited by

Actions
1. Add to My Research
2. Remove from My Research
3. E-mail
4. Print
5. Permalink
6. Citation
7. EasyBib
8. EndNote
9. RefWorks
10. Delicious
11. Export RIS
12. Export BibTeX

Title:
On-the-fly closed-loop materials discovery via Bayesian active learning
Author: Kusne, A. Gilad ; Yu, Heshan ; Wu, Changming ; Zhang, Huairuo ; Hattrick-Simpers, Jason ; DeCost, Brian ; Sarker, Suchismita ; Oses, Corey ; Toher, Cormac ; Curtarolo, Stefano ; Davydov, Albert V. ; Agarwal, Ritesh ; Bendersky, Leonid A. ; Li, Mo ; Mehta, Apurva ; Takeuchi, Ichiro
Subjects: Active learning ; Bayesian analysis ; Celestial mechanics ; characterization and analytical techniques ; Design of experiments ; Economic impact ; Impact analysis ; information storage ; Inorganic compounds ; Learning algorithms ; Machine learning ; MATERIALS SCIENCE ; Nanocomposites ; Optimization ; Phase change materials ; Scientists ; Synchrotrons ; X-ray diffraction
Is Part Of: Nature communications, 2020-11, Vol.11 (1), p.5966-5966, Article 5966
Description: Abstract Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.
Publisher: London: Nature Publishing Group
Language: English
Identifier: ISSN: 2041-1723
EISSN: 2041-1723
DOI: 10.1038/s41467-020-19597-w
PMID: 33235197
Source: Open Access: DOAJ Directory of Open Access Journals
Open Access: PubMed Central
AUTh Library subscriptions: ProQuest Central

Back to results list


INSPIRE LIBRARY - TON DUC THANG UNIVERSITY	(84-028) 37 755 057	Feedback
19 Nguyen Huu Tho St. Dist.7, HCM	thuvien@tdtu.edu.vn	Feedback

On-the-fly closed-loop materials discovery via Bayesian active learning

Searching Remote Databases, Please Wait