https://iopscience.iop.org/article/10.1088/2634-4386/ad0fec

Abstract
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.

Videos:

https://odysee.com/@medicine_rtcl_tv:7/oxygenvacancy_paperproposes_hafniumoxide_oxidelayers_varyinglevels_memristordevice_devicebased_RTCLTV_shorts:d 00:29

https://odysee.com/@social_rtcl_tv:a/resistiveswitching_memristor_electrochemicalsynthesis_resistivememory_neuromorphiccomputing_sensor_RTCLTV_shorts:7 00:33

https://odysee.com/@social_rtcl_tv:a/integratedoptics_opticalcomputingoperation_opticalneuralnetwork_photonicmultiplexing_RTCLTV_shorts:4 00:53

https://odysee.com/@stem_rtcl_tv:7/2Dnanoribbons_carbondots_flexibleelectronicdevice_memristor_self%E2%80%90assembly_RTCLTV:5 01:06

Thanks, I hate liquid/slime robots: https://youtu.be/VmV3m0QqNOY?feature=shared 07:35

History:
https://interestingengineering.com/innovation/the-history-of-robots-from-the-400-bc-archytas-to-the-boston-dynamics-robot-dog