Topological Methods for Learning to Steer Self-Organised Growth
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Informatics
Abstract
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).
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).
