Vasudevan, Rama K and Orozco, Erick and Kalinin, Sergei V (2022) Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model. Machine Learning: Science and Technology, 3 (4). 04LT03. ISSN 2632-2153
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Abstract
The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases microstructural models underpinning materials functionality are available and well understood. However, optimization of average properties via microstructural engineering often leads to combinatorically intractable problems. Here, we explore the use of the reinforcement learning (RL) for microstructure optimization targeting the discovery of the physical mechanisms behind enhanced functionalities. We illustrate that RL can provide insights into the mechanisms driving properties of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we find that non-trivial phenomena emerge if the rewards are assigned to favor physically impossible tasks, which we illustrate through rewarding RL agents to rotate polarization vectors to energetically unfavorable positions. We further find that strategies to induce polarization curl can be non-intuitive, based on analysis of learned agent policies. This study suggests that RL is a promising machine learning method for material design optimization tasks, and for better understanding the dynamics of microstructural simulations.
Item Type: | Article |
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Subjects: | Eurolib Press > Multidisciplinary |
Depositing User: | Managing Editor |
Date Deposited: | 09 Jul 2023 03:31 |
Last Modified: | 10 Oct 2023 05:29 |
URI: | http://info.submit4journal.com/id/eprint/2243 |