Topological Methods for Learning to Steer Self-Organised Growth

学习引导自组织增长的拓扑方法

• 批准号: EP/X017753/1
• 负责人: Subramanian Ramamoorthy
• 金额: $ 25.77万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX017753%2F1
• 关键词: Topological; Methods; Learning; Steer; Self

Self-organisation and self-assembly, i.e. spontaneous organisation of entities into patterns, are at the heart of growth and structure formation in nature. It is the most cost-effective way to produce larger structures for functional materials and devices. Nanoparticle self-assembly is one of the few practical strategies for making nanostructures/ patterns. One promising approach to this is an inverse statistical-mechanics method which 'designs' the optimal interaction potential between components in 2D self-assembly starting from a desired pattern. It is a major open challenge to extend such an inverse design methodology to map directly from meso-scale and bulk properties of application interest to specific properties of the real nanoparticle. Automating such a methodology would be a major step forward from today's highly manual design processes which restrict research to the small laboratory scale. Key bottlenecks on the path to automation are the topological complexity of the two-way mapping, and the significant expense of resulting Monte Carlo approaches that further include expensive ab initio simulations of the multi-scale dynamics.We envision three major innovations towards solving these problems. Firstly, we will develop new generative modelling approaches that could replace expensive ab-initio simulations of self-assembly across scales. Using the paradigm of Graph Neural Networks which have been successfully used to describe complex systems, we will develop new structured models that accurately capture the topology of structure-change dynamics in self-assembly, incorporating as inductive bias graph grammars, and multi-scale abstractions connecting levels of dynamics. Secondly, we will develop new methods for topological characterisation of the self-organising surface and use these in calibrating simulators to data, as well as to devise new algorithms for search and design optimisation. Finally, a major innovation will be in combining these to conduct optimisation not only in-silico, but directly and sample efficiently over runs of physical assembly through calibrated models and their use in topology-driven hence weakly controlled interventions to steer runs of the self-assembly process. This is enabled by efficient topological characterisation of multi-scale structure-property maps, which in turn leads to fast simulation-based inference to achieve flexible control over types of patterns that can be generated. Through collaboration between the PI, who is an AI and robotics specialist, and Co-I who is a soft matter physicist in an engineering department, we will demonstrate this methodology end-to-end by applying the developed models and optimisation methods in experiments involving functionalised nanoparticles in the laboratory, leveraging access to state-of-the-art experimental facilities including Atomic Force Microscopy and Scanning Electron Microscopy (which will be used to validate models), and to computational models at the molecular scale (to generate large scale datasets).

自我组织和自组装，即自发组织将实体组织成模式，是生长和结构形成的核心。它是为功能材料和设备生产较大结构的最具成本效益的方法。纳米颗粒自组装是制造纳米结构/模式的少数几个实用策略之一。一种有前途的方法是一种反统计力学方法，它“设计” 2D自组装中组件之间的最佳相互作用势从期望的模式开始。将这种反向设计方法扩展到直接从应用程序级和批量的批量绘制到真实纳米颗粒的特定特性直接映射到直接映射到实际纳米颗粒的特定特性，这是一个主要的开放挑战。自动化这种方法将是当今高度手动设计过程迈出的重要一步，该过程将研究限制在小型实验室范围内。自动化道路上的关键瓶颈是双向映射的拓扑复杂性，以及由此产生的蒙特卡洛方法的巨大费用，进一步包括对多规模动力学的昂贵的昂贵依据模拟。我们设想解决这些问题的三项主要创新。首先，我们将开发新的生成建模方法，以取代跨尺度上自组装的昂贵AB-Initio模拟。使用已成功用来描述复杂系统的图形神经网络的范式，我们将开发新的结构化模型，这些模型可以准确捕获自组装中结构变化动态的拓扑，并将其作为电感偏置图语法和多规模抽象连接动力学水平。其次，我们将开发新的方法，用于自我组织表面的拓扑表征，并将其用于将模拟器校准数据，并设计新算法以进行搜索和设计优化。最后，一项重大创新将是将它们结合起来，不仅在核中进行优化，而且直接并通过校准模型在物理组装上进行了有效的样品，并在拓扑驱动的驱动驱动型中进行了薄弱，从而导致了自组装过程的操作。这是通过对多尺度结构 - 特质图的有效拓扑表征来启用的，这又导致基于快速的基于模拟的推断，以实现对可以生成的模式类型的灵活控制。通过AI和机器人专家PI之间的合作，以及在工程部门中的软件物理学家的Co-I，我们将通过应用开发的模型和优化方法在涉及实验室中的实验性设施中访问的实验性工程（包括实验性的contopoy contos contosiripertion contopoy contopoy contopy cormopy contopy otterifient cormosion contopoy cormopy of Atomic cortopoy contopy cortopoy contopoy cormopy），我们将通过将开发的模型和优化方法应用于端到端，以实验性的实验性（包括在内的摩擦量）来证明这种方法论。模型），以及分子量表的计算模型（生成大型数据集）。