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Deep Reinforcement Learning for Multi-Phase Microstructure Design

Jiongzhi Yang, Srivatsa Harish, Candy Li, Hengduo Zhao, Brittney Antous, Pinar Acar*

Virginia Polytechnic Institute and State University, Blacksburg, 24061, VA, USA

* Corresponding Author: Pinar Acar. Email:

Computers, Materials & Continua 2021, 68(1), 1285-1302.


This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D printed periodic microstructures. These algorithms focus on improving the mechanical and thermal performance of engineering components by optimizing a microstructural architecture to meet different design requirements. Additionally, the machine learning solutions demonstrated equivalent results to the physics-based simulations while significantly improving the computational time efficiency. The outcomes of the project show promise to the automation of the design and manufacturing of microstructures to enable their fabrication in large quantities with the utilization of the 3-D printing technology.


Cite This Article

J. Yang, S. Harish, C. Li, H. Zhao, B. Antous et al., "Deep reinforcement learning for multi-phase microstructure design," Computers, Materials & Continua, vol. 68, no.1, pp. 1285–1302, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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