分子自主探路器使用深度强化学习学习
Ken-Ichi Nomura1, Ankit Mishra1, Tian Sang1
1Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States.
The journal of physical chemistry letters
|May 9, 2024
概括
我们开发了一个人工智能框架,使用深度强化学习来发现无形材料中的节能扩散途径. 这种方法揭示了原子扩散机制,克服了玻璃状固体中的挑战.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 固体中的扩散对于化学反应至关重要,但由于缺乏定义的路径,在无形材料中了解得很少.
- 材料故障通常与玻璃状固体中的缓慢扩散过程有关.
- 预测无形材料中的扩散机制带来了重大的计算挑战.
研究的目的:
- 开发一种人工智能引导的模拟方法,以发现无形固体中的节能扩散途径.
- 为了实现扩散过程的长期原子模拟.
- 为了弥合缓慢扩散和玻璃材料中的材料故障之间的差距.
主要方法:
- 提出了分子自主探路器 (MAP) 框架,利用深度强化学习 (DRL).
- 采用了Deep Q-Network架构,配备了分布式优先重复缓冲区,以实现高效的代理培训.
- 使用异步剂进行加速体验采样,并采用一条一条的弹性带方法来精制能量配置.
主要成果:
- 在MAP框架中,成功地确定了无形中的原子扩散路径及其能量配置.
- 实现了与实验观测可比的模拟时间尺度.
- 演示了人工智能在无序材料中发现复杂的扩散机制的能力.
结论:
- 由人工智能引导的MAP框架为研究无形材料中的扩散提供了一种有效的方法.
- 这种方法可以加速对扩散控制过程和材料降解的理解.
- 能够在具有挑战性的玻璃系统中准确预测扩散机制和时间尺度.
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