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In recent work we reported the vibrational spectrum of more than 100,000known protein structures, and a self-consistent sonification method to renderthe spectrum in the audible range of frequencies (Extreme Mechanics Letters,2019). Here we present a method to transform these molecular vibrations intomaterialized vibrations of thin water films using acoustic actuators, leadingto complex patterns of surface waves, and using the resulting macroscopicimages in further processing using deep convolutional neural networks.Specifically, the patterns of water surface waves for each protein structure isused to build training sets for neural networks, aimed to classify and furtherprocess the patterns. Once trained, the neural network model is capable ofdiscerning different proteins solely by analyzing the macroscopic surface wavepatterns in the water film. Not only can the method distinguish different typesof proteins (e.g. alpha-helix vs hybrids of alpha-helices and beta-sheets), butit is also capable of determining different folding states of the same protein,or the binding events of proteins to ligands. Using the DeepDream algorithm,instances of key features of the deep neural network can be made visible in arange of images, allowing us to explore the inner workings of protein surfacewave patter neural networks, as well as the creation of new images by findingand highlighting features of protein molecular spectra in a range ofphotographic input. The integration of the water-focused realization ofcymatics, combined with neural networks and especially generative methods,offer a new direction to realize materiomusical "Inceptionism" as a possibledirection in nano-inspired art. The method could have applications fordetecting different protein structures, the effect of mutations, or uses inmedical imaging and diagnostics, with broad impact in nano-to-macrotransitions.

Liquified protein vibrations, classification and cross-paradigm de novo image generation using deep neural networks