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To build energy minimized superstructures, self-assembling molecules explore astronomical options, colliding ∼109 molecules s−1. Thus far, no computer has used it fully to optimize choices and execute advanced computational theories only by synthesizing supramolecules. To realize it, first, we remotely re-wrote the problem in a language that supramolecular synthesis comprehends. Then, all-chemical neural network synthesizes one helical nanowire for one periodic event. These nanowires self-assemble into gel fibers mapping intricate relations between periodic events in any-data-type, the output is read instantly from optical hologram. Problem-wise, self-assembling layers or neural network depth is optimized to chemically simulate theories discovering invariants for learning. Subsequently, synthesis alone solves classification, feature learning problems instantly with single shot training. Reusable gel begins general-purpose computing that would chemically invent suitable models for problem-specific unsupervised learning. Irrespective of complexity, keeping fixed computing time and power, gel promises a toxic-hardware-free world.

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• Creative Commons BY Attribution 4.0 International
  

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キーワード: organic computer, Optical vortex, Non-algorithmic computing, helical nanowire, Single shot learning, Deep learning network, Clique problem

刊行年月日: 2023-12-01

出版者: IOP Publishing

掲載誌:

• Neuromorphic Computing and Engineering (ISSN: 26344386) vol. 3 issue. 4 044007

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1088/2634-4386/ad0fec

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更新時刻: 2024-11-13 16:30:37 +0900

MDRでの公開時刻: 2024-11-13 16:30:38 +0900