Inverse methods for design of soft materials

Abstract

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. While multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this Perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models. arXiv:2004.00181v1 [cond-mat.soft] 1 Apr 2020

Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks

“…Knowledge of such microstructures could provide new mechanistic insights, and in addition facilitate discovery of nanoparticle interactions that are most promising for experimental realization of the microstructures using inverse methods. 18,19 Due to the high-dimensional nature of the relevant search spaces, which in principle includes all possible configurations of the nanoparticles, experimental screening of candidate microstructures is often practically intractable. An attractive alternative is to solve for the microstructures using a topology optimization algorithm [20][21][22][23] that leverages structure-property predictions from a computationally tractable surrogate model.…”

Two-step crystallization and solid–solid transitions in binary colloidal mixtures

“…There is no generic approach to determine the interparticle interactions required to produce a given structure. This challenge provides a unique opportunity to integrate tools, like machine learning, to help solve the inverse design problem (41,42) and construct new pathways to crystallization.…”

Could network structures generated with simple rules imposed on a cubic lattice reproduce the structural descriptors of globular proteins?

“…However, to realize this, we need a set of well-defined rules to determine the optimal size of the grains and a meaningful texture map (orientation distributions of the grains), which we currently lack. Finally, there has been a sustained interest in the computational manipulation of targeted self-assembly (75,76) and building simple pair potentials that produce various open structures with controlled defect concentration as their ground state (77,78). Such a directed self-assembly approach is not readily applicable to proteins as it is hard to define candidate ground states with simple rules.…”