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

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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

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article

High-throughput style screening for desired material microstructures

Hui-Chia Yu, Robert Termuhlen, Affan Malik, Dirk Colbry et al.
MRS Communications
Machine Learning in Materials Science
article

High-throughput style screening for desired material microstructures

Hui-Chia Yu, Robert Termuhlen, Affan Malik, Dirk Colbry, Andreas Brandl
article en

Abstract

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

MRS CommunicationsVol. 15(5)
Michigan State University (US)
National Science Foundation, Ford Motor Company, Michigan State University, College of Engineering, Michigan State University
Openalex Percentile: Top 80%
Machine Learning in Materials Science
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