Authors: Ryo Yamada | Shigeyuki Watanabe | T Fukuoka | Satoshi Hirooka | H. Inatsu | Mina Nakagawa | H. Tada
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Chemistry — Electrochemistry

Physical Reservoir Computing with Electrochemical Reactions Machine learning based on neural networks (NN) has been used for information processing. 1 However, some disadvantages of NN are the high cost of computational resources and long training process. To overcome these limitations, physical reservoir computing (PRC) was proposed. 2 In PRC, signals are fed to PR devices that produce non-linear responses and short-term memory for input history. The outputs of the PR devices will be used as the input of the NN or a simpler network for the machine learning process. PR devices are also expected to be used as so-called edge computing devices because they can convert input signals into signals more suitable for machine learning in real time. For this purpose, time responses of PR devices should match those of the signals to be processed. The time response of PR by electrochemistry can be tuned by various parameters such as choice of reactions, concentration, temperature and hydrodynamics of solution in electrochemical cell. This wide capability would make electrochemistry as attractive and useful resource for PR devices. PRC is expected to cost less than conventional NN systems because fewer layers of NNs are required. To date, PR devices have been proposed using optical circuits, 3 dielectric relaxation in ferroelectrics, 4 spin relaxation, 5 and solid-state redox reactions. 6,7 To produce the characteristics required for PR devices, redox reactions in the liquid phase, that is, electrochemistry in solutions, are useful because many non-linear phenomena appear in simple electrochemical setups. 8,9 Several studies have reported the use of charging of electrical double layers, 10 redox reactions in solutions, 11,12 and formation of complex network structures. 13,14 However, the relationship between electrochemical reactions and PR properties has not been fully understood, even though it is essential for the rational design of electrochemical PR devices. In this study, we demonstrate PRC using the electrochemical formation and reduction of gold-oxide (herein referred to as Au oxidation-reduction for the simplicity while the reaction consists of several steps and multiple products) in aqueous solutions 15,16 with a standard three-electrode electrochemical setup. Au oxidation-reduction is easily reproducible because the gold-oxide remains on the surface of electrodes and characteristics of reactions are relatively insensitive to the structure of electrochemical cell and configuration of electrodes. These characteristics makes Au oxidation-reduction a good model system. Short-term memory is realized and tuned by adjusting the oxidation and reduction processes involved during the pulse signal transduction. An image classification task is demonstrated as an application example (Figure 1) 17 . PRC using other electrochemical reactions will be also demonstrated 1 S. Samarasinghe, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition , CRC Press, 2016. 2 G. Tanaka, et al., Neural Netw. , 2019, 115 , 100–123. 3 D. Brunner, et al., J. Appl. Phys. , 2018, 124 , 152004. 4 E. Nako, et al., in 2020 IEEE Symposium on VLSI Technology , 2020, pp. 1–2. 5 J. Torrejon, et al., Nature , 2017, 547 , 428–431. 6 M. Nakajima, et al., Nanoscale , 2022, 14 , 7634–7640. 7 Y. Usami, et al., Adv. Mater. , 2021, 33 , e2102688. 8 M. T. M. Koper, J. Chem. Soc. Faraday Trans. , 1998, 94 , 1369–1378. 9 D. Kim and J.-S. Lee, ACS Appl. Electron. Mater. , 2023, 5 , 664–673. 10 S.-G. Koh, et al., Sci. Rep. , 2022, 12 , 6958. 11 S. Kan, , et al., Adv. Sci. , 2022, 9 , e2104076. 12 T. Matsuo, et al., ACS Appl. Mater. Interfaces , 2022, 14 , 36890–36901. 13 M. Cucchi, et al., Sci Adv , , DOI:10.1126/sciadv.abh0693. 14 G. Milano, , et al., Nat. Mater. , 2022, 21 , 195–202. 15 L. D. Burke and P. F. Nugent, Gold Bull. , 1997, 30 , 43–53. 16 S. Cherevko, et al. RSC Adv. , 2013, 3 , 16516–16527. 17 R. Yamada et al., RSC Adv., 2023, 13 , 24801. Figure 1 Electrochemistry ; Computer science ; Petroleum engineering ; Geology ; Chemistry ; Electrode ; Physical chemistry Electrochemical Analysis and Applications ; Advanced Memory and Neural Computing ; Force Microscopy Techniques and Applications