Computational Physics Research Articles

Harnessing spatiotemporal transformation in magnetic domains for nonvolatile physical reservoir computing.

Combining physics with computational models is increasingly recognized for enhancing the performance and energy efficiency in neural networks. Physical reservoir computing uses material dynamics of physical substrates for temporal data processing. Despite the ease of training, building an efficient reservoir remains challenging. Here, we explore beyond the conventional delay-based reservoirs by exploiting the spatiotemporal transformation in all-electric spintronic devices. Our nonvolatile spintronic reservoir effectively transforms the history dependence of reservoir states to the path dependence of domains. We configure devices triggered by different pulse widths as neurons, creating a reservoir featured by strong nonlinearity and rich interconnections. Using a small reservoir of merely 14 physical nodes, we achieved a high recognition rate of 0.903 in written digit recognition and a low error rate of 0.076 in Mackey-Glass time series prediction on a proof-of-concept printed circuit board. This work presents a promising route of nonvolatile physical reservoir computing, which is adaptable to the larger memristor family and broader physical neural networks.

Machine learning applications to computational plasma physics and reduced-order plasma modeling: a perspective

Machine learning applications to computational plasma physics and reduced-order plasma modeling: a perspective

Preparation of 4D hydrogels with PET-RAFT and orthogonal photo-reactions

We investigated of 4D dimensional hydrogels with photo orthogonal reaction. The 4D movements were observed and discussed based on the physical properties and computer simulation.

Nonlinear memristor model with exact solution allows for ex situ reservoir computing training and in situ inference.

Memristive physical reservoir computing is a promising approach for solving data classification and temporal processing tasks. This method exploits the nonlinear dynamics of physical, low-power devices to achieve high-dimensional mapping of input signals. Ion-channel-based memristors, which operate with similar voltages, currents, and timescales as biological synapses, are promising due to their rich dynamics, especially for use in biological edge settings. Accurate modeling of their dynamics is essential for optimizing network hyperparameters ex situ to save time and energy. Here, a generalized sigmoidal growth model of ion-channel memristor conductance is presented and shown to be more accurate in predicting dynamics than linear or logistic models. Using the exact solution of the proposed sigmoidal model, the MNIST handwritten digit classification task is optimized and trained ex situ, then tested in situ with the same trained weights. This approach achieved an experimental testing accuracy of 90.6%.

Reservoir computing with generalized readout based on generalized synchronization

Reservoir computing is a machine learning framework that exploits nonlinear dynamics, exhibiting significant computational capabilities. One of the defining characteristics of reservoir computing is that only linear output, given by a linear combination of reservoir variables, is trained. Inspired by recent mathematical studies of generalized synchronization, we propose a novel reservoir computing framework with a generalized readout, including a nonlinear combination of reservoir variables. Learning prediction tasks can be formulated as an approximation problem of a target map that provides true prediction values. Analysis of the map suggests an interpretation that the linear readout corresponds to a linearization of the map, and further that the generalized readout corresponds to a higher-order approximation of the map. Numerical study shows that introducing a generalized readout, corresponding to the quadratic and cubic approximation of the map, leads to a significant improvement in accuracy and an unexpected enhancement in robustness in the short- and long-term prediction of Lorenz and Rössler chaos. Towards applications of physical reservoir computing, we particularly focus on how the generalized readout effectively exploits low-dimensional reservoir dynamics.

DEVELOPMENT OF HYDROPONICS FOR THE FORMATION OF PRACTICAL SKILLS OF THE STEM EDUCATION SUBJECTS

The article analyzes the use of a hydroponic device as a STEM education tool. STEM education is the basis of practical interest and a high level of motivation of schoolchildren for research activities and obtaining all the necessary competencies. The use of STEM technology will allow you to transfer the educational process to another basis: from observation to hypothesis and experiment, from studying individual subjects to studying phenomena, from obtaining abstract knowledge to solving real life problems. The hydroponic system as a teaching technology can be used in the study of a number of school subjects: computer science, biology, chemistry, physics and natural sciences. First of all, in order to build the design of a hydroponic installation, creative data is needed on the selection of necessary products, the ability to work with tools for connecting plastic pipes, connecting to a pump, etc. In the manufacture of the structure, it is necessary to calculate the length, height of the structure, the number of holes for pots, when connected to the pump, etc. In addition, it is necessary to calculate the amount of nutrients necessary for the growth of plants, which are controlled by changes in the acid-base environment and total salinity. The hydroponic plant can monitor the growth and development of vegetable and green crops and monitor the transport of nutrients. In all courses of such subjects as "Natural Science", "Biology", "Chemistry" and "Physics", students will have the opportunity to gain practical skills when working with hydroponic devices.

A comparative analysis of relativistic particle pushers vis-à-vis computation time & accuracy

Abstract The performance of relativistic particle pushers has long been a topic of interest in the field of computational plasma physics, particularly from the point of view of the particle-in-cell approach. Previous works undertaken to compare such integrators have predominantly targeted the ultra-relativistic regime. In this paper, we utilize a custom-built code to study the core run-times of the Boris, the Vay, and the Higuera-Cary particle pushers for low-, high-, and ultra-relativistic particles. This is followed by a comparison of the three integrators in terms of accuracy and error. A fitness parameter is then proposed that can serve as a one-stop value to determine which method is more suitable for a particular simulation setup. It is hoped that through knowledge of such intricacies, the choice for the integrator will be easier to make depending on the problem at hand.

Acceleration without Disruption: DFT Software as a Service.

Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods. This paper introduces a novel, cloud-native application, Accelerated DFT, which offers an order of magnitude acceleration in DFT simulations. By integrating state-of-the-art cloud infrastructure and redesigning algorithms for graphic processing units (GPUs), Accelerated DFT achieves high-speed calculations without sacrificing accuracy. It provides a user-friendly and scalable solution for the increasing demands of DFT calculations in scientific communities. The implementation details, examples, and benchmark results illustrate how Accelerated DFT can significantly expedite scientific discovery across various domains.

Physics teachers’ perceptions of using physical computing to develop future skills in school students

Abstract The purpose of this paper is to explore physics teachers’ perceptions of the role of physical computing in developing future skills of school students in Saudi Arabia. Quantitative data were gathered through questionnaires distributed to 45 physics teachers who had integrated physical computing in their classrooms. The study provides some insights into the variations of those perceptions based on demographic variables such as teachers’ gender, qualifications, teaching experience and type of microcontroller used. The findings suggest that physical computing has a significant role in developing future skills among students. Recommendations for employing Fourth Industrial Revolution approaches and including physical computing in teacher professional development are provided. From the viewpoint of physics teachers, this paper fills a significant gap in physical computing’s role in developing future skills among students.

Neural mechanisms underlying the effects of cognitive fatigue on physical effort-based choice.

Fatigue is a state of exhaustion that influences our willingness to engage in effortful tasks. While both physical and cognitive exertion can cause fatigue, there is a limited understanding of how fatigue in one exertion domain (e.g., cognitive) affects decisions to exert in another (e.g., physical). We use functional magnetic resonance imaging (fMRI) to measure brain activity while human participants make decisions to exert prospective physical effort before and after engaging in a cognitively fatiguing working memory task. Using computational modeling of choice behavior, we show that fatiguing cognitive exertion increases participants' subjective costs of physical effort compared to a baseline rested state. We describe how signals related to fatiguing cognitive exertion in the dorsolateral prefrontal cortex influence physical effort value computations instantiated by the insula, thereby increasing an individual's subjective valuation of prospective physical effort while cognitively fatigued. Our results support the idea of a general fatigue signal that integrates exertion-specific information to guide effort-based choice.

Proprioceptive and Exteroceptive Information Perception in a Fabric Soft Robotic Arm via Physical Reservoir Computing with Minimal Training Data

Over recent decades, soft and reconfigurable robots have rapidly emerged thanks to their ability to interact safely with humans and adapt to complex environments. . However, their softness makes accurate control challenging, requiring high‐fidelity sensing for posture and contact estimation.Traditional camera‐based sensors and load cells have limited portability and accuracy, and they will inevitably increase the robot's cost and weight. In this study, instead of using specialized sensors, only distributed pressure data inside a pneumatics‐driven soft arm are collected and the physical reservoir computing principle is applied to simultaneously predict its kinematic posture (i.e., bending angle) and payload status (i.e., payload mass). Results show that, with careful readout training, one can obtain accurate bending angle and payload mass predictions via simple, weighted linear summations of pressure readings. In addition,analysis show that, to guarantee low prediction errors within 10%, bending angle prediction requires less training data than payload prediction. This reveals that balanced linear and nonlinear body dynamics are critical for the physical reservoir to accomplish complex proprioceptive and exteroceptive information perception tasks. Finally, the method of exploring efficient readout training methods presented here could be extended to other soft robotic systems to maximize their perception capabilities.

Improving accuracy of parametric surrogate model for turbidity currents using diffusion-based super-resolution

Turbidity currents are a sort of density-driven flow carrying particles that are generated between fluids with small density differences. They also are a mechanism responsible for the deposition of sediments on a seabed. A deep understanding of this phenomenon may help geologists on strategic knowledge in oil exploration. We simulate such currents using a stabilized finite element formulation in a Eulerian-Eulerian framework.This brings the challenge of a high computational cost for the evaluation of the high-fidelity model. We thus use a surrogate model composed of one linear (Proper Orthogonal Decomposition) and one non-linear (Autoencoder) reduction [1,2]. Once the surrogate is trained for a set of parameters with data generated by the high-fidelity model, one can use such a surrogate model for predicting the dynamics for unseen values of the parameter.Denoising Diffusion Probabilistic Models (DDPM) [3] is the method behind SOTA generative AI algorithms with very good performance in tasks like super-resolution. Based on such usage of diffusion for generative AI, the authors in [4] developed a framework for flow field reconstruction using a super-resolution task. Such a diffusion model is trained only using high-resolution data, without a pairing between low and high-resolution data, common in other techniques in the area.The present work aims to use the dataset of high-fidelity parametric simulations of turbidity currents for training both the surrogate and the diffusion model for the super-resolution task. The goal is: using the trained super-resolution model, improve the fields predicted by the reduced model for unseen points of the parametric space.[1] - M. Cracco et al., “Deep learning-based reduced-order methods for fast transient dynamics”, Arxiv Preprint 2212.07737, 2022.[2] - S. Fresca and A. Manzoni, “POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition”, Comput. Methods Appl. Mech. Engrg., 2022.[3] - J. Ho, A. Jain and P. Abbeel, “Denoising Diffusion Probabilistic Models”, arXiv: 2006.11239, 2020.[4] - D. Shu, Z. Li, A. Barati Farimani, “A physics-informed diffusion model for high-fidelity flow field reconstruction”, Journal of Computational Physics, Volume 478, 2023

The development of children's gender stereotypes about STEM and verbal abilities: A preregistered meta-analytic review of 98 studies.

This meta-analysis studied the development of ability stereotypes that could limit girls' and women's participation in science, technology, engineering, and mathematics (STEM) fields, as well as contribute to boys' underachievement in reading and writing. We integrated findings from 98 studies measuring children's gender stereotypes about STEM and verbal abilities. The data comprised 145,204 children (ages 4-17) from 33 nations across more than 40 years (1977-2020). Preregistered analyses showed why prior researchers have reached diverging conclusions about the onset, change, and extent of these stereotypes in childhood and adolescence. Contrary to some prior conclusions, math stereotypes favoring male ability were minimal on average (0.11 SDs from gender neutrality). Stereotypes were instead far stronger for computer science, engineering, and physics (0.51 SDs), which favored male ability by age 6. Girls increasingly endorsed pro-male STEM stereotypes with age. Pro-female verbal ability stereotypes were also substantial (0.46 SDs), emerging by age 8 and becoming more female-biased with age. Additionally, STEM stereotypes were weaker for Black than White U.S. participants, as predicted. Unexpectedly, however, boys' STEM stereotypes declined before age 13 but increased thereafter, revealing an asymmetric development across STEM versus verbal domains. We integrated developmental intergroup theory and social role theory to explain this asymmetry, considering both cognitive and sociocultural processes. The early emergence of verbal stereotypes and certain STEM stereotypes (e.g., engineering) means that they have ample time to affect later downstream outcomes such as domain-specific confidence and interests. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Evaluation of The Stochasticity in The Collective Bernoulli-ball System as A Physical Reservoir Computer

Evaluation of The Stochasticity in The Collective Bernoulli-ball System as A Physical Reservoir Computer

Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device

AbstractThe artificial intelligence (AI) paradigm shifts from software to implementing general‐purpose or application‐specific hardware systems with lower power requirements. This study explored a material physical reservoir consisting of a material random network, called in‐materio physical reservoir computing (RC) to achieve efficient hardware systems. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions as reservoir nodes, demonstrated the requisite characteristics of an in‐materio physical reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. The power consumption of the in‐materio physical reservoir is 0.07 nW per nanojunctions, confirming its highly efficient information processing system. As a hardware reservoir, the devices successfully performed waveform generation tasks. Finally, a voice classification by an in‐materio physical reservoir is achieved over 80%, comparable to an RC software simulation. In‐materio physical RC with rich nonlinear dynamics has huge potential for next‐generation hardware‐based AI.

Physical Reservoir Computing in a Music Hall Experiment

Abstract Music is a complex vibratory structure that evolves temporally and, while it has been studied for centuries, both quantitatively and qualitatively, it has seldom been studied under the lens of computer science and information theory. Further, while much research has been devoted to measuring and optimizing the acoustics of music venues, the computational ability of these spaces has never been explored. Using physical reservoir computing, this paper presents an experimental verification that a music hall has computational ability. Two experimental setups are explored: one has a single speaker and information is sent sequentially, and another has two speakers and information is sent simultaneously. Both of these exhibit qualitatively similar results. Thus, music might be, at least in part, a computational experience. The findings of this paper could provide a computational basis for the upper limit of tactus in music. To the authors' knowledge, this is the first time that a music hall has been utilized as a computational resource. Moreover, the computational ability of musical structures provides another tool to understand the complex relationship between music, vibrations, and human perception.

A Physics‐Driven GraphSAGE Method for Physical Field Simulations Described by Partial Differential Equations

AbstractPhysics‐informed neural networks (PINNs) have successfully addressed various computational physics problems based on partial differential equations (PDEs). However, while tackling issues related to irregularities like singularities and oscillations, trained solutions usually suffer low accuracy. In addition, most current works only offer the trained solution for predetermined input parameters. If any change occurs in input parameters, transfer learning or retraining is required, and traditional numerical techniques also need recomputation. In this work, a physics‐driven GraphSAGE approach (PD‐GraphSAGE) based on the Galerkin method and piecewise polynomial nodal basis functions is presented to solve computational problems governed by irregular PDEs and to develop parametric PDE surrogate models. This approach employs graph representations of physical domains, thereby reducing the demands for evaluated points due to local refinement. A distance‐related edge feature and a feature mapping strategy are devised to help training and convergence for singularity and oscillation situations, respectively. The merits of the proposed method are demonstrated through a couple of cases. Moreover, the robust PDE surrogate model for heat conduction problems parameterized by the Gaussian random field source is successfully established, which not only provides the solution accurately but is several times faster than the finite element method in the experiments.

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 demonstrated1 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

Iono-Magnonic Reservoir Computing Utilizing Interfered Spin Wave Manipulated By Ion-Gating

Physical reservoir computing is a promising candidate for implementing high-performance artificial intelligence devices, mimicking biological system by using nonbiological components. The reservoir computing network has fewer learning parameters than conventional deep learning, and the advantage leads to high-speed processing and low electric power consumption. Since the physical reservoir plays a vital role in mapping input information depending on past state nonlinearly into a high dimensional space, the physical device must possess nonlinearity, short-term memory, and high dimensionality. However, some issues (i.e., high electrical power consumption, insufficient computational performance, and large volumes) still need to be addressed in achieving practical physical reservoir.Our previous work revealed that nonlinear interfered spin wave multi-detection exhibits high computational performance on a second-order nonlinear autoregressive moving average (NARMA2) task.[1] While the spin wave is a Joule-loss-free information carrier, and its interference gives a reservoir rich expressive power (e.g., chaos), the computational performance is still inferior to an optoelectrical reservoir. The best way to overcome this problem is to manipulate the spin wave in situ.Herein, we fabricated an iono-magnonic reservoir device to let the research fields of ionics and magnonics collaborate. This scheme is built on the basis of ion-gating, which can drastically manipulate magnetic property through redox reaction triggered by gate voltage (V G) application.[2,3] This study is the first study of manipulating chaotic spin wave interference as the information carrier, achieved with a solid-state electrolyte and its application for high-performance reservoir computing.[4]Figure A shows the fabricated physical reservoir, which consists of a Y3Fe5O12 (YIG) single crystal and a proton-conducting Nafion, and its measurement configuration. Two exciters for interference and two detectors for multi-detection are deposited on the YIG. Protons in Nafion migrate to the YIG by V G application, and in situ magnetic property manipulation due to electron doping is expected.Figure B shows the depth dependence of energy loss measured by electron energy-loss spectroscopy. While the energy loss of a Nafion/YIG to which V G was applied ('biased') at the bulk region was in good agreement with that of a Nafion/YIG to which V G was not applied (‘unbiased’), the energy loss at the Nafion/YIG interface region shifted to the lower energy side, indicating that Fe ion near the interface was electrochemically reduced from trivalent to divalent states. V G dependence of M S and H a is shown in Fig. C. M S of 1984.6 Gauss at V G = 0.0 V is in good agreement with M S of 1984.0 Gauss obtained from magnetization measurement in a pristine YIG single crystal. H a at V G = 0.0 V is 1586.7 Oe. Increasing V G, corresponding to electron doping, decreases both M S and H a. The change saturates at the region of V G ≥ 1.6 V. Spin wave frequency variations at various V G are summarized in Fig. D. The frequencies under magnetic fields of 170 mT increase from approximately 1.3 GHz to 1.32 GHz, corresponding to a shift of 17.9 MHz, and the increase ratios are 1.38 %. This modulated spin wave can achieve a variety of reservoirs and may contribute to improving the computational performance.Figure E shows a schematic concept of reservoir computing. The network was constructed by reservoir layers connected in parallel by utilizing the spin wave property modulated by ion-gating. Input time-series data is transformed nonlinearly to waveforms by 800 nodes (X 1 - X 800) in reservoir layers connected in parallel through utilizing spin waves modulated by eight V G states. Then, these nodes are crossed by 800 output weights W out to generate a reservoir output. The computational error of the NARMA2 task is 9.53 x 10-3, which corresponds to reducing 47.3 %, compared to the computational performance of the nonlinear interfered spin wave multi-detection, and the computational error is much lower than that of the reservoir utilizing the optoelectronic system, as shown in Fig. F. This drastic improvement results from excellent nonlinearity of the chaotic spin wave interference and the ability to map in higher dimensional space by ion-gating, and the iono-magnonic reservoir updated the best performance in other physical reservoirs reported thus far. The iono-magnonic reservoir leads to achievement in high-performance artificial intelligence devices.This work was partially supported by Innovative Science and Technology Initiative for Security Grant Number JPJ004596, ATLA, Japan, JST PRESTO (JPMJPR23H4), and JSPS KAKENHI Grant Numbers JP22H04625 and JP19H05814 (Grant-in-Aid for Scientific Research on Innovative Areas “Interface Ionics”).

Neuromorphic Computing Based on Few-Molecule Vibration Dynamics Achieved By Surface-Enhanced Raman Scattering and Ion-Gating Stimulation

1. ObjectiveArtificial 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. ExperimentsFigure 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 DiscussionAlthough 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.