Publications

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional Cu_xV₂O₅ framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

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.

Selectorless resistive random-access memory is essential for scaling high-density crossbar arrays, yet suppressing sneak path currents (SPCs) without external selector components remains a major challenge. In this work, we investigated a two-dimensional (2D) van der Waals (vdW) mixed cation crystal Cu_0.5Ag_0.5InP₂S₆ (CAIPS) as a switching layer and systematically compared its resistive switching with CuInP₂S₆ (CIPS) and AgInP₂S₆ (AIPS). The coexistence of Cu⁺ and Ag⁺ ions produces asymmetric out-of-plane diffusion barriers, as confirmed by first-principles density functional theory (DFT) calculations, leading to self-rectifying transport and the intrinsic suppression of leakage in arrays. CAIPS-based devices exhibit stable bipolar resistive switching, a high intrinsic nonlinearity factor (>10 under a V/3 read scheme), a large memory window (>9× at V_read = 0.1 V), and low variability (coefficient of variation down to 5.1%), surpassing the performance of both CuInP₂S₆ (CIPS) and AgInP₂S₆ (AIPS). These features, combined with low operational switching voltages, robust endurance, and built-in nonlinearity highlight CAIPS as a promising material for scalable selectorless memory arrays, with direct relevance to energy-efficient neuromorphic and edge-computing architectures.

There is growing interest in correlated oxides that can switch between volatile resistance states when an electrical bias is applied, functioning as artificial neurons in neuromorphic computing systems. Most devices typically rely on first-order insulator–metal transitions (IMT). However, recent discoveries have shown that devices made of a second-order spin-transition material, such as LaCoO₃ (LCO), can exhibit different or improved functionalities. Despite their significance, the microscopic details surrounding the formation of conductive channels have still been unreported. In this study, the spatiotemporal details of channel formation are revealed by using a combination of infrared (IR) and Raman microscopy. Comparison of LCO and materials such as VO₂ reveals critical differences with important ramifications for computing. First, the findings indicate that LCO channels are narrower and more efficient than VO₂, but they are also more sensitive to electric fields and disorder. Channels are found to repeatedly hop between different locations under steady-state oscillations, a behavior not previously reported. Additionally, memory effects at high bias are observed. The experiments, along with finite element simulations (FES), suggest that the spin transition in LCO may significantly influence channel nucleation, leading to an increased sensitivity of neuronal devices to disorder and electrode geometry. We discuss how the inherent stochasticity and memory effects could enable functionalities in neuromorphic computing.