Neuroevolutionary learning of particles and protocols for self-assembly

Abstract

Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without input from physical concepts such as thermal equilibrium or mechanical stability and without prior knowledge of candidate or competing structures. The learning algorithm is capable of both directed and exploratory design: it can assemble a material with a user-defined property, or search for novelty in the space of specified order parameters. In the latter mode it explores the space of what can be made rather than the space of structures that are low in energy but not necessarily kinetically accessible.

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Cited by in corpus (9)

• Designing the self-assembly of arbitrary shapes using minimal complexity building blocks
• SAT-assembly: A new approach for designing self-assembling systems
• Programmable patchy particles for materials design
• A simple solution to the problem of self-assembling cubic diamond crystals
• Assembly of Complex Colloidal Systems Using DNA
• Multi-objective optimization for targeted self-assembly among competing polymorphs
• Two-step nucleation in a binary mixture of Patchy Particles
• Genetic-tunneling driven energy optimizer for spin systems
• Deposition control of model glasses with surface-mediated orientational order