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<p>In the era of big data, reinforcement learning (RL) has emerged as a powerful data-driven optimization approach in materials science, enabling unprecedented advances in material design and performance improvement. Unlike traditional trial-and-error and physics-based approaches, RL agents autonomously identify optimal strategies across high-dimensional and dynamic design spaces by iterative interactions with complex environments. This capability makes RL especially effective for target optimization and sequential decision-making in challenging materials science problems. In this review, we present a comprehensive overview of fundamental RL algorithms, including Q-learning, deep Q-networks (DQN), actor-critic methods, and deep deterministic policy gradient (DDPG). Then, the core mechanisms, advantages, limitations, and representative applications of RL in materials discovery, property optimization, process control, and manufacturing are discussed systematically. Lastly, key future research directions and opportunities are outlined. The perspectives presented herein aim to foster interdisciplinary collaboration and drive innovation at the frontier of AI‑driven materials science.</p>

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Reinforcement Learning in Materials Science: Recent Advances, Methodologies and Applications

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Jiaye Li,
  • Xinyuan Zhang,
  • Chunlei Shang,
  • Xing Ran,
  • Zhe Wang,
  • Chengjiang Tang,
  • Xiaohang Zhang,
  • Mingshuo Nie,
  • Wei Xu,
  • Xin Lu

摘要

In the era of big data, reinforcement learning (RL) has emerged as a powerful data-driven optimization approach in materials science, enabling unprecedented advances in material design and performance improvement. Unlike traditional trial-and-error and physics-based approaches, RL agents autonomously identify optimal strategies across high-dimensional and dynamic design spaces by iterative interactions with complex environments. This capability makes RL especially effective for target optimization and sequential decision-making in challenging materials science problems. In this review, we present a comprehensive overview of fundamental RL algorithms, including Q-learning, deep Q-networks (DQN), actor-critic methods, and deep deterministic policy gradient (DDPG). Then, the core mechanisms, advantages, limitations, and representative applications of RL in materials discovery, property optimization, process control, and manufacturing are discussed systematically. Lastly, key future research directions and opportunities are outlined. The perspectives presented herein aim to foster interdisciplinary collaboration and drive innovation at the frontier of AI‑driven materials science.