High-throughput style screening for desired material microstructures
Abstract This work presents a high-throughput microstructure simulation framework for use in the discipline of computational materials science to screen out desirable structures. As motivation, we present a brief study of battery microstructural architecture optimization. Realistic 3D microstructures are reconstructed from voxel data. The voxel data can be easily modified, enabling rapid manipulation and making high-throughput investigation possible. The microstructure-level simulations are made computationally feasible by using the Smoothed Boundary Method to embed complicated microstructures in the computational domain. Additionally, our efforts toward an open-source microscale battery simulation software, titled BESFEM, are previewed. Graphical Abstract
Authors
- Hui-Chia Yu (ORCID: https://orcid.org/0000-0002-4351-3581)
- Robert Termuhlen
- Affan Malik (ORCID: https://orcid.org/0000-0001-5223-4560)
- Dirk Colbry (ORCID: https://orcid.org/0000-0003-0666-9883)
- Andreas Brandl (ORCID: https://orcid.org/0000-0003-1990-2584)
Institutions
- Michigan State University (US)
Publication Details
- Journal
- MRS Communications
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1557/s43579-025-00826-6
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Ford Motor Company
- Michigan State University
- College of Engineering, Michigan State University