التفاصيل البيبلوغرافية
| العنوان: |
Experimental Ni₃TeO₆ synthesis condition exploration accelerated by active learning |
| المؤلفون: |
Botella, R. (R.), Fernández-Catalá, J. (J.), Cao, W. (W.) |
| بيانات النشر: |
Elsevier |
| سنة النشر: |
2023 |
| المجموعة: |
Jultika - University of Oulu repository / Oulun yliopiston julkaisuarkisto |
| مصطلحات موضوعية: |
Crystalline materials, Machine learning, Ni₃TeO₆, Optimization synthesis, Phase diagram |
| الوصف: |
Material synthesis is time- and chemicals-consuming due to the traditional (“brute force”) methodology. For instance, Ni₃TeO₆ (NTO) is a multiferroic material relevant in different applications. Herein, we used an active learning scheme to explore the different phases obtained using a complex hydrothermal synthesis procedure instead of a solid-state methodology. Different from conventional ML prediction requiring a large dataset, we show that with only 9 data points obtained through experimental endeavor, 87% of the experimental condition space is predicted. The predicted phase configuration is verified with the sample in a new synthetic work. Beside exploring the NTO species, scheme developed herein constitute a powerful tool for experimental condition optimization. |
| نوع الوثيقة: |
article in journal/newspaper |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
info:eu-repo/grantAgreement/EC/H2020/101002219/EU/Cross-dimensional Activation of Two-Dimensional Semiconductors for Photocatalytic Heterojunctions/CATCH |
| الاتاحة: |
http://urn.fi/urn:nbn:fi-fe20231010139427 |
| Rights: |
info:eu-repo/semantics/openAccess ; © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ; https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.4ABC94E2 |
| قاعدة البيانات: |
BASE |