使用智能活性粒子混合二进制被动粒子系统.
Thomas Jacob1,2, Siddhant Mohapatra1, Rajalingam A1
1Department of Mechanical Engineering, Indian Institute of Technology Madras, 600036, Chennai, India.
Scientific reports
|December 20, 2025
概括
由人工神经网络指导的智能活性粒子,实现了被动粒子的优越混合. 最佳策略涉及局部活动区域,增强系统级现象,用于材料和药物输送中的应用.
科学领域:
- 物理,软物质和材料科学 物理,软物质和材料科学
- 计算机科学,人工智能和机器学习
背景情况:
- 活性物质系统表现出新兴现象,在药物输送,材料科学和微流体学中具有应用.
- 积极和被动实体之间的受控相互作用是利用这些新兴属性的关键.
研究的目的:
- 为了实现分离的被动颗粒的最佳混合,使用一小部分智能活性颗粒.
- 调查适应性学习在活性物质系统中的作用,以提高性能.
主要方法:
- 由训练有素的人工神经网络 (ANN) 代理人指导的具有适应性行为的活性粒子的引入.
- 传统的运行和的粒子与ANN引导的活性粒子之间的混合效率的比较.
- 分析粒子运动动态和空间分布,以获得最佳的混合策略.
主要成果:
- 与传统方法相比,ANN引导活性颗粒显示出明显更快,更有效的混合.
- 最佳的混合不是通过均分散来实现,而是通过将活性颗粒活动集中在一个异常区域.
- 这种局部活动诱导了被动粒子的全球旋转运动,并观察到向中心的方向变化.
结论:
- 整合机器学习,特别是ANN,提高了活性物质系统的控制和性能.
- 适应性活性粒子为优化复杂系统中的混合和其他新出现现象提供了强大的工具.
- 这项研究将活性物质物理学和人工智能联系起来,为微流体学和适应性材料的新应用铺平了道路.
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