Towards peptide-based tunable multistate memristive materials

Development of new memristive hardware is a technological requirement towards widespread neuromorphic computing. Molecular spintronics seems to be a fertile field for the design and preparation of this hardware. Within molecular spintronics, recent results on metallopeptides demonstrating the interaction between paramagnetic ions and the chirality induced spin selectivity effect hold particular promise for developing fast (ns–μs) operation times. [R. Torres-Cavanillas et al., J. Am. Chem. Soc., 2020, DOI: 10.1021/jacs.0c07531]. Among the challenges in the field, a major highlight is the difficulty in modelling the spin dynamics in these complex systems, but at the same time the use of inexpensive methods has already allowed progress in that direction. Finally, we discuss the unique potential of biomolecules for the design of multistate memristors with a controlled- and indeed, programmable-nanostructure, allowing going beyond anything that is conceivable by employing conventional coordination chemistry.ERC-CoG DECRESIM 647301COST-MOLSPIN-CA15128CTQ2017-8952CEX2019-000919-MPrometeo Program of ExcellencePRECOMP14-202646Development of new memristive hardware is a technological requirement towards widespread neuromorphic computing. Molecular spintronics seems to be a fertile field for the design and preparation of this hardware. Within molecular spintronics, recent results on metallopeptides demonstrating the interaction between paramagnetic ions and the chirality induced spin selectivity effect hold particular promise for developing fast (ns–μs) operation times. [R. Torres-Cavanillas et al., J. Am. Chem. Soc., 2020, DOI: 10.1021/jacs.0c07531]. Among the challenges in the field, a major highlight is the difficulty in modelling the spin dynamics in these complex systems, but at the same time the use of inexpensive methods has already allowed progress in that direction. Finally, we discuss the unique potential of biomolecules for the design of multistate memristors with a controlled- and indeed, programmable-nanostructure, allowing going beyond anything that is conceivable by employing conventional coordination chemistry

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

Few-molecule reservoir computing experimentally demonstrated with surface enhanced Raman scattering and ion-gating stimulation

Reservoir computing (RC) is a promising solution for achieving low power consumption neuromorphic computing, although the large volume of the physical reservoirs reported to date has been a serious drawback in their practical application. Here, we report the development of a few-molecule RC that employs the molecular vibration dynamics in the para-mercaptobenzoic acid (pMBA) detected by surface enhanced Raman scattering (SERS) with tungsten oxide nanorod/silver nanoparticles (WOx@Ag-NPs). The Raman signals of the pMBA molecules, adsorbed at the SERS active site of WOx@Ag-NPs, were reversibly perturbated by the application of voltage-induced local pH changes in the vicinity of the molecules, and then used to perform RC of pattern recognition and prediction tasks. In spite of the small number of molecules employed, our system achieved good performance, including 95.1% to 97.7% accuracy in various nonlinear waveform transformations and 94.3% accuracy in solving a second-order nonlinear dynamic equation task. Our work provides a new concept of molecular computing with practical computation capabilities.Comment: 22 pages, 4 figure

Chiral spintronics

As spins move through a chiral electric field, the resulting spin current can acquire chirality through a spin–orbit interaction. Such spin currents are highly useful in creating spin–orbit torques that can be used to manipulate chiral topological magnetic excitations, for example, chiral magnetic domain walls or skyrmions. When the chiral domain walls form composite domain walls, via an antiferromagnetic exchange coupling, novel phenomena, including an exchange coupling torque and domain wall drag, are observed. Here, we review recent progress in the generation and functionalities of spin currents derived from or acting on chiral structures. By bringing together advances in chiral molecules, chiral magnetic structures and chiral topological matter, we provide an outlook towards potential applications

Memristors for the Curious Outsiders

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page

Investigation of wide bandgap semiconductors for room temperature spintronic, and photovoltaic applications

Suitability of wide bandgap semiconductors for room temperature (RT) spintronic, and photovoltaic applications is investigated. Spin properties of metal-organic chemical vapor deposition (MOCVD) – grown gadolinium-doped gallium nitride (GaGdN) are studied and underlying mechanism is identified. GaGdN exhibits Anomalous Hall Effect at room temperature if it contains oxygen or carbon atoms but shows Ordinary Hall Effect in their absence. The mechanism for spin and ferromagnetism in GaGdN is a combination of intrinsic, metallic conduction, and carrier-hopping mechanisms, and is activated by oxygen or carbon centers at interstitial or similar sites. A carrier-related mechanism in MOCVD-grown GaGdN at room temperature makes it a suitable candidate for spintronic applications. Zinc oxide (ZnO) doped with transition metals such as nickel and manganese and grown by MOCVD is investigated, and bandgap tunability is studied. A bandgap reduction with transition metal doping is seen in ZnO with dilute doping of nickel or manganese. Transition metals could introduce energy states in ZnO that result in a bandgap reduction and could be tuned and controlled by growth conditions and post-growth processing such as annealing, for spintronic and photovoltaic applications”--Abstract, page iii