Sherman Zachary M, Howard Michael P, Lindquist Beth A, Jadrich Ryan B, Truskett Thomas M
McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas 78712, USA.
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
J Chem Phys. 2020 Apr 14;152(14):140902. doi: 10.1063/1.5145177.
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 a 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.
功能性软材料由胶体和分子构建块组成，这些构建块由于其可调节的相互作用而自组织成复杂结构，从而实现了广泛的技术应用。逆方法提供了一种系统的手段，用于在其固有的高维设计空间中导航，以创建具有目标特性的材料。虽然多种基于物理的逆策略已在计算机模拟中成功实施，但迄今为止，它们在指导实验材料发现方面的应用仅限于少数概念验证研究。从这个角度出发，我们讨论了软材料设计逆方法的最新进展，这些进展解决了两个挑战：（1）方法上的局限性，这些局限性阻止了此类方法满足设计约束；（2）计算上的挑战，这些挑战限制了可处理系统的规模和复杂性。利用机器学习的策略已被证明特别有效，包括发现表征复杂结构基序的序参量的方法，以及从底层结构有效计算宏观性质的方案。我们还强调了提高计算设计材料实验可实现性的有前景的机会，包括发现具有多种热力学状态功能的材料、设计易于在实验中实施的外部定向组装协议，以及提高实验相关模型的准确性和计算效率的策略。
