Engineered nanoparticle network models for autonomous computing

Materials that exhibit synaptic properties are a key target for our effort to develop computing devices mimic the brain intrinsically. If successful, they could lead high performance, low energy consumption, and huge data storage. A 2D square array of engineered nanoparticles (ENPs) interconnected by an emergent polymer network is possible candidate. Its behavior has been observed characterized using coarse-grained molecular dynamics (CGMD) simulations analytical lattice models. Both models consistent in predicting links at varying temperatures, free volumes, E-field (E⃗) strengths. Hysteretic behavior, short-term plasticity long-term plasticity—necessary brain-like storage computing—have CGMD ENP networks response E-fields. Non-volatility were also confirmed be robust perturbations dielectric constant, temperature, affine geometry.