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).

自组织和自组装，即实体自发组织成模式，是自然界生长和结构形成的核心。这是生产功能材料和设备的更大结构的最具成本效益的方法。纳米粒子自组装是制造纳米结构/图案的少数实用策略之一。一个有前途的方法，这是一个逆几何力学方法，“设计”的最佳相互作用的组件之间的潜力，在2D自组装从一个所需的模式。这是一个主要的开放性挑战，扩展这样的逆向设计方法，直接从应用感兴趣的介观尺度和散装性能映射到真实的纳米粒子的特定性能。自动化这样的方法将是一个重要的一步，从今天的高度手动设计过程，限制研究的小实验室规模。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Monte Carlo方法的显着费用，进一步包括昂贵的从头计算模拟的多尺度dynamics.We设想三个主要的创新解决这些问题。首先，我们将开发新的生成建模方法，可以取代跨尺度自组装的昂贵从头算模拟。使用已成功用于描述复杂系统的图形神经网络的范例，我们将开发新的结构化模型，准确地捕捉自组装中结构变化动态的拓扑结构，结合归纳偏置图文法和连接动态层次的多尺度抽象。其次，我们将开发新的方法的自组织表面的拓扑特征，并使用这些校准模拟器的数据，以及设计新的算法搜索和设计优化。最后，一个重大的创新将是将这些结合起来，不仅在计算机上进行优化，而且通过校准模型直接有效地对物理组装的运行进行采样，并将其用于拓扑驱动的，因此控制较弱的干预措施，以引导自组装过程的运行。这是通过多尺度结构-性质图的高效拓扑特征来实现的，这反过来又导致基于模拟的快速推理，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与工程系软物质物理学家Co-I之间的合作，我们将通过在实验室中涉及功能化纳米颗粒的实验中应用开发的模型和优化方法，端到端地展示这种方法，利用最先进的实验设施，包括原子力显微镜和扫描电子显微镜（其将用于验证模型）和分子尺度的计算模型（以生成大规模数据集）。