| DOI | Resolve DOI: https://doi.org/10.1103/PhysRevLett.127.018003 |
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| Author | Search for: Whitelam, Stephen; Search for: Tamblyn, Isaac1ORCID identifier: https://orcid.org/0000-0002-8146-6667 |
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| Affiliation | - National Research Council of Canada. Security and Disruptive Technologies
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| Format | Text, Article |
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| 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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| Publication date | 2021-06-29 |
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| Publisher | American Physical Society |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 78b33787-213a-4c00-8cb0-042637c1e9d9 |
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| Record created | 2022-02-02 |
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| Record modified | 2022-02-04 |
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