Polyanichenko Dmitry S, Protsenko Bogdan O, Egil Nikita V, Kartashov Oleg O
The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia.
Materials (Basel). 2023 Jul 28;16(15):5321. doi: 10.3390/ma16155321.
The most in-demand instrumental methods for new functional nanomaterial diagnostics employ synchrotron radiation, which is used to determine a material's electronic and local atomic structure. The high time and resource costs of researching at international synchrotron radiation centers and the problems involved in developing an optimal strategy and in planning the control of the experiments are acute. One possible approach to solving these problems involves the use of deep reinforcement learning agents. However, this approach requires the creation of a special environment that provides a reliable level of response to the agent's actions. As the physical experimental environment of nanocatalyst diagnostics is potentially a complex multiscale system, there are no unified comprehensive representations that formalize the structure and states as a single digital model. This study proposes an approach based on the decomposition of the experimental system into the original physically plausible nodes, with subsequent merging and optimization as a metagraphic representation with which to model the complex multiscale physicochemical environments. The advantage of this approach is the possibility to directly use the numerical model to predict the system states and to optimize the experimental conditions and parameters. Additionally, the obtained model can form the basic planning principles and allow for the optimization of the search for the optimal strategy with which to control the experiment when it is used as a training environment to provide different abstraction levels of system state reactions.
用于新型功能纳米材料诊断的最热门仪器方法是利用同步辐射，同步辐射用于确定材料的电子结构和局部原子结构。在国际同步辐射中心进行研究的时间和资源成本高昂，且在制定优化策略和规划实验控制方面存在的问题十分严峻。解决这些问题的一种可能方法是使用深度强化学习智能体。然而，这种方法需要创建一个特殊环境，该环境能对智能体的行动提供可靠的响应水平。由于纳米催化剂诊断的物理实验环境可能是一个复杂的多尺度系统，所以不存在将结构和状态形式化为单个数字模型的统一综合表示。本研究提出了一种基于将实验系统分解为原始物理上合理的节点的方法，随后将其合并并优化为一种元图形表示，以此来对复杂的多尺度物理化学环境进行建模。这种方法的优点是可以直接使用数值模型来预测系统状态，并优化实验条件和参数。此外，当所获得的模型用作训练环境以提供不同抽象层次的系统状态反应时，它可以形成基本的规划原则，并有助于优化寻找控制实验的最优策略。
