| DOI | Trouver le DOI : https://doi.org/10.1103/PhysRevLett.127.018003 |
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| Auteur | Rechercher : Whitelam, Stephen; Rechercher : Tamblyn, Isaac1Identifiant ORCID : https://orcid.org/0000-0002-8146-6667 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies de sécurité et de rupture
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| Format | Texte, Article |
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| Résumé | 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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| Date de publication | 2021-06-29 |
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| Maison d’édition | American Physical Society |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 78b33787-213a-4c00-8cb0-042637c1e9d9 |
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| Enregistrement créé | 2022-02-02 |
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| Enregistrement modifié | 2022-02-04 |
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