Casert Corneel, Whitelam Stephen
Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA.
Department of Physics and Astronomy, Ghent University, 9000, Ghent, Belgium.
Nat Commun. 2024 Oct 23;15(1):9128. doi: 10.1038/s41467-024-52878-2.
Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.
精确的解析计算表明，被动分子系统的最优控制协议通常涉及快速变化和不连续性。然而，活性物质系统通常没有类似的解析基线，因为精确处理活性系统更加困难。在这里，我们使用机器学习来推导活性物质系统的有效控制协议，并发现它们具有与被动系统中类似的尖锐特征。我们表明，通过以神经网络的形式对协议进行编码，有可能在活性粒子的模拟模型中学习到能够实现快速高效的状态到状态转换的协议。我们使用进化方法来识别能够使活性粒子尽可能快地或尽可能少地消耗能量从一个稳态转变到另一个稳态的协议。我们的结果表明，由灵活的神经网络假设所识别的协议比最近通过约束解析方法推导的协议更有效，这种假设允许对多个控制参数进行优化并出现尖锐特征。我们的学习方案在实验中易于使用，为在实验室中高效操纵活性物质设计协议提供了一种方法。
