Probabilistic inverse design for self-assembling materials

• DOI: 10.1063/1.4981796
• Publication Date: 2017-05-09T14:45:27Z
• Authors: R. B. Jadrich; B. A. Lindquist; T. M. Truskett
• Keywords: Condensed Matter - Materials Science; 0103 physical sciences; Materials Science (cond-mat.mtrl-sci); Soft Condensed Matter (cond-mat.soft); FOS: Physical sciences; 02 engineering and technology; Condensed Matter - Soft Condensed Matter; 0210 nano-technology; 01 natural sciences

One emerging approach for the fabrication of complex architectures on nanoscale is to utilize particles customized intrinsically self-assemble into a desired structure. Inverse methods statistical mechanics have proven particularly effective discovery interparticle interactions suitable this aim. Here we evaluate generality and robustness recently introduced inverse design strategy [Lindquist et al., J. Chem. Phys. 145, 111101 (2016)] by applying simulated-based, machine learning method optimize that variety microstructures: cluster fluids, porous mesophases, crystalline lattices. Using method, discover isotropic pair lead self-assembly each morphologies, including several types potentials were not previously understood be capable stabilizing such systems. potential led assembly highly asymmetric truncated trihexagonal lattice another produced fluid containing spherical voids, or pores, designed size via purely repulsive interactions. Through these examples, demonstrate advantages inherent particular use parametrized functional form optimized interactions, ability constrain range said parameters, compatibility with simulation protocols (e.g., positional restraints).