Neuroevolutionary Learning of Particles and Protocols for Self-Assembly.
Phys Rev Lett
; 127(1): 018003, 2021 Jul 02.
Article
en En
| MEDLINE
| ID: mdl-34270312
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Automático
/
Modelos Químicos
Tipo de estudio:
Health_economic_evaluation
Idioma:
En
Revista:
Phys Rev Lett
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Estados Unidos