Neuromorphic Computing Based on Few-Molecule Vibration Dynamics Achieved By Surface-Enhanced Raman Scattering and Ion-Gating Stimulation
Authors: Nishioka Daiki , Shingaya Yoshitaka , Terabe Kazuya , Tsuchiya Takashi 
Published: December 2024

1. Objective
Artificial intelligence (AI), which has achieved high-level capabilities in cognition, learning, and inference through technological innovations such as deep learning, has greatly benefited human society, however, problems such as high energy consumption and increased communication traffic have also emerged. As a solution to these problems, physical reservoir computing (PRC), a kind of neuromorphic computing that uses the nonlinear behavior of physical systems to efficiently process information, has been attracting attention.[1-5] Although various methods of PRC have been reported (e.g., memristors, electrochemical cells, optical circuits, soft bodies, and spintronics devices), the molecular computing approach is promising to realize the ultimate compact and integrated PRC. However, the number of molecules in a conventional molecular reservoir system is huge, and the information processing capability of a single molecule to several molecules has not been elucidated.[2,3] In this study, we developed a few-molecule reservoir computing (FM-RC) that utilizes the molecular vibration dynamics of single to few molecules of para-mercaptobenzoic acid (pMBA). The information processing capability of few molecules was evaluated by performing information processing tasks such as blood glucose level prediction on FM-RC. [4]
2. Experiments
Figure 1a shows a schematic diagram of the measurement system of FM-RC. In order to accurately track the nonlinear dynamics of a few molecules, surface-enhanced Raman scattering (SERS) measurements using WOx nanorods/silver nanoparticles (WOx@Ag-NPs) were performed, and molecular vibration reflecting the structural change of pMBA based on the local proton adsorption change due to ion-gating stimulation was observed in the molecular dynamics (Fig. 1b) reflecting the conformational changes of pMBA based on the local proton adsorption changes induced by ion-gating stimuli. The obtained SERS spectra were regarded as independent artificial neurons (nodes) at specific sampling wavenumbers, and these node states were used to perform various information processing tasks (Fig. 1c).
3. Results and Discussion
Although FM-RC consists of only a few molecules, it achieved a high accuracy of 95.1% to 97.7% in the nonlinear waveform transformation task and 94.3% in the analysis task of second-order nonlinear equations, and achieved the highest computational performance among physical reservoirs in the task of predicting blood glucose levels for type I pediatric diabetes patients (Fig. 1d).[4-6] These high computational performances are attributed to the independent nonlinearity of the nodes distributed in the wavenumber direction, i.e., the high dimensionality achieved by the complex and diverse response characteristics of each vibrational mode to the proton adsorption amount. In this presentation, the origin of the high computational performance of FM-RC will be discussed in terms of nonlinearity and high dimensionality, which are the main properties that determine the performance of PRCs.
References
[1] D. Nishioka et al., Sci. Adv.
8, eade1156 (2022). [2] A. Goudarzi et al, DNA Reservoir Computing (Springer, 2013). [3] Y. Usami Adv. Mater.
33, 2102688 (2021). [4] D. Nishioka et al., Sci. Adv.
10, eadk6438 (2024). [5] T. Shingu et al., Carbon
214, 118344 (2023). [6] DIRECNET Study Group, Diabetes Technol. Therapeut.
5, 781-789 (2003).
Acknowledgments
This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP22H04625 (Grant-in-Aid for Scientific Research on Innovative Areas “Interface Ionics”), Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP22KJ2799 (Grant-in-Aid for JSPS Fellows), JST PRESTO (grant number, JPMJPR23H4), and the Iketani Science and Technology Foundation.

Figure 1