Toward a multiscale theoretical framework for organic memristive materials

Abstract Neuromorphic computing aspires to overcome the intrinsic inefficiencies of von Neumann architectures by co-locating memory and computation in physical devices that emulate biological neurons and synapses. Memristive materials stand at the core of this paradigm, enabling non-volatile, history-dependent electronic responses. While inorganic oxides currently dominate the field, molecular and polymeric systems can offer untapped advantages in terms of chemical tunability, structural flexibility, low-cost processing, and biocompatibility. These organic memristive materials have emerged as promising building blocks for neuromorphic computing, yet their rational design is hindered by the absence of a coherent theoretical framework capable of bridging molecular-scale processes with macroscopic device behavior. Rather than proposing a new theory per se, this Perspective articulates how established multiscale theoretical and computational approaches can be systematically adapted and integrated to address the specific challenges posed by organic and molecular memristive systems. Three mechanisms-ionic migration, redox-driven switching, and conduction interplay in chiral molecules are examined as representative routes toward molecular neuromorphic hardware. By clarifying the transfer of physically meaningful parameters across computational scales and highlighting open challenges related to stability, variability, and data scarcity, this framework aims to guide future theoretical and experimental efforts in the field.