Physics-informed models of domain wall dynamics as a route for autonomous domain wall design <i>via</i> reinforcement learning-Reference-Cited by-同舟云学术

Physics-informed models of domain wall dynamics as a route for autonomous domain wall design via reinforcement learning

Published:2024 Issue:3 Volume:3 Page:456-466
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Smith Benjamin R.¹²^ORCID,Pant Bharat³^ORCID,Liu Yongtao²,Liu Yu-Chen⁴,Yang Jan-Chi⁴^ORCID,Jesse Stephen²,Khojandi Anahita⁵^ORCID,Kalinin Sergei V.⁶,Cao Ye³,Vasudevan Rama K.²^ORCID

Affiliation:

1. Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, TN 37996, USA

2. Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA

3. Department of Materials Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA

4. Department of Physics, National Cheng Kung University, Tainan 70101, Taiwan

5. Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA

6. Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA

Abstract

Prompted by limited available data, we explore data-aggregation strategies for material datasets, aiming to boost machine learning performance. Our findings suggest that intuitive aggregation schemes are ineffective in enhancing predictive accuracy.

Funder

U.S. Department of Energy

Oak Ridge National Laboratory

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00126A

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