Multi-objective optimization for targeted self-assembly among competing polymorphs
arXiv:2401.11234 · doi:10.1103/PhysRevX.15.011075
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.
References in corpus (12)
- Designed Interaction Potentials via Inverse Methods for Self-Assembly
- Inverse methods for design of soft materials
- Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments
- Inverse Design for Self Assembly via On-the-Fly Optimization
- Designing patchy interactions to self-assemble arbitrary structures
- Active learning of the thermodynamics-dynamics tradeoff in protein condensates
- Macroscopic DNA-programmed photonic crystals via seeded growth
- Temperature Protocols to Guide Selective Self-Assembly of Competing Structures
- Comprehensive view of microscopic interactions between DNA-coated colloids
- Neuroevolutionary learning of particles and protocols for self-assembly
- Assembly of Complex Colloidal Systems Using DNA
- Optimization of Non-Equilibrium Self-Assembly Protocols Using Markov State Models