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DOE OSTI2024

Photoinduced Melting of V 4 O 7 Correlated State

The compound V 4 O 7 is one of the Magnéli phase (V n O 2n - 1 , n = 3, 4, …, 9) correlated vanadium oxides with distinct intriguing electronic and structural properties. The possibility to manipulate the phase state of V 4 O 7 on an ultrafast time scale by light makes this material promising for potential applications in photonics, optoelectronics, quantum, and neuromorphic circuit design. In this work, the ultrafast spectroscopy of V 4 O 7 reveals the second-order nature of the photoinduced insulator-to-metal transition, emphasizing electronic and lattice contributions. The findings reveal the influence of the laser excitation level and temperature on these dynamics, providing a comprehensive understanding of V 4 O 7 structural changes and response to external stimuli. The phenomenological model based on the Landau–Ginzburg formalism provides a robust framework for explaining the photoinduced transition dynamics, showing a detailed picture of the light interaction with the electronic and lattice subsystems. This integrated approach significantly enhances the understanding of V 4 O 7 complex behavior upon photoexcitation, opening new possibilities for developing new optoelectronic devices and noninvasive optical control of the phase transition pathways in vanadates.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Skip-Connected Self-Recurrent Spiking Neural Networks with Joint Intrinsic Parameter and Synaptic Weight Training

As an important class of spiking neural networks (SNNs), recurrent spiking neural networks (RSNNs) possess great computational power and have been widely used for processing sequential data like audio and text. However, most RSNNs suffer from two problems. 1. Due to the lack of architectural guidance, random recurrent connectivity is often adopted, which does not guarantee good performance. 2. Training of RSNNs is in general challenging, bottlenecking achievable model accuracy. To address these problems, we propose a new type of RSNNs called Skip-Connected Self-Recurrent SNNs (ScSr-SNNs). Recurrence in ScSr-SNNs is introduced in a stereotyped manner by adding self-recurrent connections to spiking neurons. The SNNs with self-recurrent connections can realize recurrent behaviors similar to those of more complex RSNNs while the error gradients can be more straightforwardly calculated due to the mostly feedforward nature of the network. The network dynamics is enriched by skip connections between nonadjacent layers. Moreover, we propose a new backpropagation (BP) method called backpropagated intrinsic plasticity (BIP) to further boost the performance of ScSr-SNNs by training intrinsic model parameters. Unlike standard intrinsic plasticity rules that adjust the neuron's intrinsic parameters according to neuronal activity, the proposed BIP method optimizes intrinsic parameters based on the backpropagated error gradient of a well-defined global loss function in addition to synaptic weight training. Here, based on challenging speech, neuromorphic speech, and neuromorphic image datasets, the proposed ScSr-SNNs can boost performance by up to 2.85% compared with other types of RSNNs trained by state-of-the-art BP methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Backpropagation-based learning with local derivative approximation and memory replay in biologically plausible neural systems

When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.

42 ENGINEERING↗

DOE OSTI2024

Hidden magnetism and split off flat bands in the insulator metal transition in VO 2

Transition metal d -electron oxides with an odd number of electrons per unit cell are expected to form metals with partially occupied energy bands, but exhibit in fact a range of behaviors, being either insulators, or metals, or having insulator-metal transitions. Traditional explanations involved predominantly electron-electron interactions in fixed structural symmetry. The present work focuses instead on the role of symmetry breaking local structural motifs. Viewing the previously observed V-V dimerization in VO 2 as a continuous knob, reveals in density functional calculations the splitting of an isolated flat band from the broad conduction band. This leads past a critical percent dimerization to the formation of the insulating phase while lowering the total energy. In VO 2 this transition is found to have a rather low energy barrier approaching the thermal energy at room temperature, suggesting energy-efficient switching in neuromorphic computing. Interestingly, sufficient V-V dimerization suppresses magnetism, leading to the nonmagnetic insulating state, whereas magnetism appears when dimerization is reduced, forming a metallic state. This study opens the way to design novel functional quantum materials with symmetry breaking-induced flat bands.

Chemistry↗

DOE OSTI2023

Intermittent Defect Fluctuations in Oxide Heterostructures

Abstract The heterogeneous nature, local presence, and dynamic evolution of defects typically govern the ionic and electronic properties of a wide variety of functional materials. While the last 50 years have seen considerable efforts into development of new methods to identify the nature of defects in complex materials, such as the perovskite oxides, very little is known about defect dynamics and their influence on the functionality of a material. Here, the discovery of the intermittent behavior of point defects (oxygen vacancies) in oxide heterostructures employing X‐ray photon correlation spectroscopy is reported. Local fluctuations between two ordered phases in strained SrCoO x with different degrees of stability of the oxygen vacancies are observed. Ab‐initio‐informed phase‐field modeling reveals that fluctuations between the competing ordered phases are modulated by the oxygen ion/vacancy interaction energy and epitaxial strain. The results demonstrate how defect dynamics, evidenced by measurement and modeling of their temporal fluctuations, give rise to stochastic properties that now can be fully characterized using coherent X‐rays, coupled for the first time to multiscale modeling in functional complex oxide heterostructures. The study and its findings open new avenues for engineering the dynamical response of functional materials used in neuromorphic and electrochemical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

In situ synchrotron x-ray studies of epitaxial SrCoO x films during ionic liquid gating

The manipulation of ions in complex oxide materials can be used to mimic brain-like plasticity through changes to the resistivity of a neuromorphic device. Advances in the design of more energy efficient devices require improved understanding of how ions migrate within a material and across its interface. We investigate the exchange of oxygen and hydrogen in a model SrCoO x epitaxial film—a material that transitions between a ferromagnetic metal and antiferromagnetic insulator depending on the oxygen concentration. Changes to the film during ionic liquid gating were measured by in situ synchrotron x-ray techniques as a function of time and gate voltage, examining the reversibility of the oxide over one complete gating cycle. We find that the out-of-plane lattice constant and oxygen vacancy concentration of SrCoO x are largely reversible although changes were observed in the ordered vacancy structure. Our results provide much needed insight into electrolyte-gated phase behavior in the transition metal oxides.

Li, Yan [Argonne National Laboratory (ANL), Argonn↗

DOE OSTI2025

Bypassing Fast Time Scales of the Hodgkin-Huxley Neuron Model via a Thresholded Hard Reset

We propose a modification to the Hodgkin-Huxley model to reduce the numerical stiffness of the equations by introducing an explicit voltage threshold. When this threshold is crossed, the voltage and the gating variables are reset to constant values. It is found that, for all of the current stimuli considered, the proposed model accurately reproduces the behavior of the baseline Hodgkin-Huxley model while bypassing the fast dynamics of spiking. Specifically, the model accurately reproduces the spike times and, between spikes, the time courses of the membrane potential and gating variables.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Evidence for long-term potentiation in phospholipid membranes

Biological supramolecular assemblies, such as phospholipid bilayer membranes, have been used to demonstrate signal processing via short-term synaptic plasticity (STP) in the form of paired pulse facilitation and depression, emulating the brain’s efficiency and flexible cognitive capabilities. However, STP memory in lipid bilayers is volatile and cannot be stored or accessed over relevant periods of time, a key requirement for learning. Using droplet interface bilayers (DIBs) composed of lipids, water and hexadecane, and an electrical stimulation training protocol featuring repetitive sinusoidal voltage cycling, we show that DIBs displaying memcapacitive properties can also exhibit persistent synaptic plasticity in the form of long-term potentiation (LTP) associated with capacitive energy storage in the phospholipid bilayer. The time scales for the physical changes associated with the LTP range between minutes and hours, and are substantially longer than previous STP studies, where stored energy dissipated after only a few seconds. STP behavior is the result of reversible changes in bilayer area and thickness. On the other hand, LTP is the result of additional molecular and structural changes to the zwitterionic lipid headgroups and the dielectric properties of the lipid bilayer that result from the buildup of an increasingly asymmetric charge distribution at the bilayer interfaces.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

In Situ Studies of the Swelling by an Electrolyte in Electrochemical Doping of Ethylene Glycol-Substituted Polythiophene

Organic mixed ionic electronic conductors (OMIECs) have the potential to enable diverse new technologies, ranging from biosensors to flexible energy storage devices and neuromorphic computing platforms. However, a study of these materials in their operating state, which convolves both passive and potential-driven solvent, cation, and anion ingress, is extremely difficult, inhibiting rational material design. In this report, we present a novel approach to the in situ studies of the electrochemical switching of a prototypical OMIEC based on oligoethylene glycol (oEG) substitution of semicrystalline regioregular polythiophene via grazing-incidence X-ray scattering. Here by studying the crystal lattice both dry and in contact with the electrolyte while maintaining potential control, we can directly observe the evolution of the crystalline domains and their relationship to film performance in an electrochemically gated transistor. Despite the oEG side-chain enabling bulk electrolyte uptake, we find that the crystalline regions are relatively hydrophobic, exhibiting little (less than one water per thiophene) swelling of the undoped polymer, suggesting that the amorphous regions dominate the reported passive swelling behavior. With applied potential, we observe that the π–π separation in the crystals contracts while the lamella spacing increases in a balanced fashion, resulting in a negligible change in the crystal volume. The potential-induced changes in the crystal structure do not clearly correlate to the electrical performance of the film as an organic electrochemical transistor, suggesting that the transistor performance is strongly influenced by the amorphous regions of the film.

36 MATERIALS SCIENCE↗

DOE OSTI2022

All-Electric Nonassociative Learning in Nickel Oxide

Habituation and sensitization represent nonassociative learning mechanisms in both non-neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular-like learning in oxygen-deficient NiO x devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiO x under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next-generation neuroinspired computing hardware.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Ion-Electron Coupling-Driven Redox Behavior in Metal–Organic Frameworks

Redox-active metal–organic frameworks (MOFs) have long been proposed as electronic transport platforms, yet the microscopic origin of their conductivity remains debated. A theoretical demonstration reveals charge transport in a Zn(pyrazole–naphthalene diimide (NDI)) MOF arising not from delocalized band-like states but from redox hopping between discrete linker sites. Using ab initio molecular dynamics simulations combined with electronic structure analysis, we established a direct link among electron injection, structural reorganization, and transport. Electron accumulation proceeds sequentially and site-selectively from imide and carbonyl groups of the NDI core progressively involving pyrazole N atoms at higher reduction states, through a hierarchy of redox-active sites. In contrast, Zn nodes remain essentially redox-inactive, which confirms their structural role. Density-of-states analysis corroborates a transport regime dominated by linker-centered states with evolving p-character upon reduction, resulting in dynamically reconfigured conduction networks. Real-time trajectories reveal anisotropic linker-to-linker electron transfer modulated by counterion coordination. This cooperative ion–electron regime emerges from potential energy surface collapse into a single low-barrier transition (ΔG ‡ ≈ 45 meV), where ionic and electronic motions evolve adiabatically on the same free-energy landscape. Elucidating redox conductivity in Zn(pyrazole–NDI) MOFs provides a theoretical framework for use in neuromorphic computing and related technologies.

Charge transfer↗

DOE OSTI2025

Interactions Enhance Ramp Reversal Memory in Locally Phase Separated Materials

The ramp-reversal memory (RRM) effect in metal–insulator transition metal oxides (TMOs), a non-volatile resistance change induced by repeated temperature cycling, has attracted considerable interest in neuromorphic computing and non-volatile memory devices. Our previous defect motion model successfully explained RRM in vanadium dioxide (VO 2 ), capturing observed critical temperature shifts and memory accumulation throughout the sample. However, this approach lacked interactions between metallic and insulating domains. Here, we extend our model by combining a correlated Random Field Ising Model with defect diffusion-segregation, enabling accurate hysteresis modeling while predicting the relationship between RRM and domain interactions. Our simulations demonstrate that the maximum RRM occurs when the turnaround temperature approaches the inflection point. This peak in RRM vs. turnaround temperature is consistent with prior transport measurements, as well as our own optical measurements reported here. Significantly, we find that increasing nearest-neighbor interactions enhances the maximum memory effect, thus providing a clear mechanism for optimizing RRM performance. Since our model employs minimal assumptions, we predict that RRM should be a widespread phenomenon in materials exhibiting patterned phase coexistence of electronic domains. This work not only advances fundamental understanding of memory behavior in TMOs but also establishes a much-needed theoretical framework for optimizing device applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Self‐Assembled NiO Nanocrystal Arrays as Memristive Elements

Abstract Memristive switching, a nonlinear current–voltage ( I – V ) characteristic, has seen a tremendous surge in interest as an approach to achieve implementation of synaptic functions. The memristive switching behavior of self‐assembled NiO nanocrystals is investigated via scanning probe microscopy, based on first‐order reversal curve current–voltage spectroscopy. Synaptic switching is clearly observed as a direct consequence of filament growth (i.e., gradually increased conductance) in the nanocrystals. A spatial dependency of the conduction in the nanocrystals suggests that there is a localization of the switching filament. The current understanding of this localization ignores features related to local lateral variation current, which can generate an excessive local heat and temperature such as electrical dissipation. The observation of low electrical dissipation at the edge of the nanocrystals shows that less energy is wasted as heat such that the bias applied can be utilized more efficiently to assist the nucleation of the filament and thus reduces the power consumption. Electrical power dissipation is also found to scale with nanocrystal height and has spatial dependence within the nanocrystals. The combination of synaptic switching and high density of the nanocrystals demonstrate that it is feasible to exploit them to create a basic architecture for neuromorphic memory devices.

Kurnia, Fran↗

DOE OSTI2026

Material‐Driven Neuronal Oscillators and Filters via Active Reactance in CC‐NDR and VC‐NDR Electro‐Thermal Memristors

The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.

active reactance↗

DOE OSTI2026

Relaxor Ferroelectric-Like Spatiotemporal Memory in Field-Driven Lipid Bilayers

Lipid membranes are often regarded as passive barriers, yet their nonlinear dielectric response remains poorly understood. Using all-atom molecular dynamics, we show that fully hydrated dipalmitoylphosphatidylcholine bilayers exhibit relaxor ferroelectric-like behavior under time-dependent electric fields. Unlike crystalline relaxors, which are bipolar and display little remanent polarization, lipid bilayers exhibit a unipolar polarization response: even an alternating current field produces persistent, asymmetric polarization. Furthermore, the underlying free-energy landscape contains two distinct minima, a nonpolarized state and a unipolarly polarized state, between which stochastic thermally activated transitions occur. Directionally resolved Van Hove analysis reveals pronounced anisotropy arising from out-of-plane electric dipole alignment, interleaflet coupling, and lateral polarization domains. Each field cycle nucleates polarization at distinct sites and monitors their relaxation, marking a crossover from thermal fluctuations to field-sustained polarization. Remarkably, these polarized domains persist after field removal, generating long-lived, spatially coherent dipolar patterns that encode nanoscale polarization memory. Potassium chloride amplifies these effects via dielectric screening and a modified hydration structure, enhancing electric dipole flexibility and cooperativity. Together, these results establish protein-free bilayers as nonlinear, history-dependent dielectrics capable of sustaining field-tunable electromechanical coupling, providing an emergent physical foundation for nanoscale information storage and memory phenomena reminiscent of short- and long-term plasticity in soft neuromorphic systems.

Insulators↗

DOE OSTI2024

Subnanometer Scale Mapping of Hydrogen Doping in Vanadium Dioxide

Hydrogen donor doping of correlated electron systems such as vanadium dioxide (VO 2 ) profoundly modifies the ground state properties. The electrical behavior of H x VO 2 is strongly dependent on the hydrogen concentration; hence, atomic scale control of the doping process is necessary. It is however a nontrivial problem to quantitatively probe the hydrogen distribution in a solid matrix. As hydrogen transfers its sole electron to the material, the ionization mechanism is suppressed. Here in this study, a methodology mapping the doping distribution at subnanometer length scale is demonstrated across a H x VO 2 thin film focusing on the oxygen-hydrogen bonds using electron energy loss spectroscopy (EELS) coupled with first-principles EELS calculations. The hydrogen distribution was revealed to be nonuniform along the growth direction and between different VO 2 grains, calling for intricate hydrogenation mechanisms. Our study points to a powerful approach to quantitatively map dopant distribution in quantum materials relevant to energy and information science

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Electrolytic gold plating, stripping, and ion transport dynamics through a solid-state iodide perovskite

The pronounced electrochemical reactivity between halide perovskites and metal electrodes can introduce mobile extrinsic metal ions which can cause device instability or enable novel functionalities. Here we systematically investigate the kinetics of gold cation (Au + ) migration in indium tin oxide (ITO)/methylammonium lead triiodide (MAPbI 3 )/Au model devices under long-term potentiostatic biasing. Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) analyses reveal that Au + ions, electrochemically generated at the Au anode, traverse the perovskite layer with diffusion coefficients on the order of 10 −11 to 10 −10 cm 2 s −1 and are subsequently reduced at the cathode as Au 0 clusters, resembling metal plating behavior in electrolytic cells and solid-state batteries during charging. Furthermore, reversing the applied bias strips the plated Au 0 , revealing reversibility suitable for bipolar resistive switching devices and providing direct evidence of the electrochemical and ionic nature of Au transport within the perovskite matrix. Quantitatively determining diffusion coefficients and ion concentrations provides foundational inputs for future drift-diffusion modelling opportunities and allows us to relate our findings to implications on long term operation of devices like photovoltaic modules. These results clearly demonstrate the solid-state electrochemical nature of perovskite devices, highlight methods to be more quantitative about ion transport properties, provide and emphasize the importance of disentangling electro-, photo-, photoelectrochemical processes for understanding device performance and unlocking new functionalities.

14 SOLAR ENERGY↗