Exploring Electrochemical Hysteresis in Organic Mixed Ionic–Electronic Conductors via a Multiscale Predictive Framework

Electrochemical hysteresis in organic mixed ionic–electronic conductors (OMIECs) arises from coupled ionic retention, electronic transport, and bias history. Engineering hysteresis is pivotal for devices such as neuromorphic and memory systems that require robust, programmable windows; hence a predictive framework is needed. We present a generalizable multiscale framework that links atomistic molecular dynamic simulations to continuum device models. Molecular dynamics provides specific material properties, like sieve entropy, dielectric permittivity, diffusion/segmental mobility, and doping-related properties, which inform a variational Poisson–Nernst–Planck model solved self-consistently. Applying device-relevant waveforms as boundary conditions yields hysteretic current-voltage loops and scan-rate-dependent memory windows. Preliminary results across representative OMIEC families show that a single pipeline recovers qualitative loop shapes and clarifies ionic–electronic mechanisms throughout the scan. Ongoing work expands the framework's universality and benchmarks design rules to suppress hysteresis for sensing or exploit it for memory. The full workflow and validation will be presented.