Multi-objective optimization for targeted self-assembly among competing polymorphs

TL;DR

This paper introduces a machine learning-based method to optimize interaction potentials for self-assembly, balancing thermodynamic stability and kinetic accessibility to favor desired polymorphs while revealing inherent trade-offs.

Contribution

It presents a novel multi-objective optimization algorithm that considers both thermodynamics and kinetics in self-assembly design, highlighting fundamental trade-offs.

Findings

Optimal potentials lie along a Pareto front.

Trade-offs exist between speed of assembly and stability.

Kinetic optimization can stabilize competing polymorphs.

Abstract

Most approaches for designing self-assembled materials focus on the thermodynamic stability of a target structure or crystal polymorph. Yet in practice, the outcome of a self-assembly process is often controlled by kinetic pathways. Here we present an efficient machine learning-guided design algorithm to identify globally optimal interaction potentials that maximize both the thermodynamic yield and kinetic accessibility of a target polymorph. We show that optimal potentials exist along a Pareto front, indicating the possibility of a trade-off between the thermodynamic and kinetic objectives. Although the extent of this trade-off depends on the target polymorph and the assembly conditions, we generically find that the trade-off arises from a competition among alternative polymorphs: The most kinetically optimal potentials, which favor the target polymorph on short timescales, tend to stabilize a competing polymorph at longer times. Our work establishes a general-purpose approach for multi-objective self-assembly optimization, reveals fundamental trade-offs between crystallization speed and defect formation in the presence of competing polymorphs, and suggests guiding principles for materials design algorithms that optimize for kinetic accessibility.