The Assembly of Molecular Memristors for Energy-Efficient Computing (AMMEC) project is organized into three thrusts, each of which focuses on filling a key knowledge gap currently preventing the predictive design of biologically inspired polyoxometalate (POM)-peptoid-based molecular memristors for energy-efficient neuromorphic computing.
Thrust 1 – Johnson
Thrust 1 focuses on establishing the fundamental principles that guide the synthesis and assembly of POM-peptoid hybrids with programmable resistive states, as well as developing an understanding of the electronic properties of these materials. Thrust 1 insights are foundational to the practical development of POM-peptoid based memristors. We hypothesize that the short-range interactions of POMs with sequence-defined peptoids on two-dimensional solid interfaces determine the energy levels and stability of resistive states.
Thrust 2 – Chen
Thrust 2 centers on macromolecular assembly, developing an understanding of how to predictively tune the overall structure of POM-peptoid hybrids to produce targeted responses. We will experimentally and computationally investigate the influence of morphology, POM-peptoid-substrate interactions, crystallinity, POM ordering and alignment, and POM microenvironment on the electronic properties of POM-peptoid hybrids. We hypothesize that intermolecular and surface interactions between and within the POM and peptoid will determine the nanoscale structure and long-range order of the assemblies. The order and structure are predicted to be key regulators of the memristive states and electron and ion transport in the POM-peptoid hybrids.
Thrust 3 – Liang
Thrust 3 combines work from Thrusts 1 and 2, focusing on the production and study of assembled memristor devices. We will explore how different assemblies and structures of POM-peptoid hybrid-based memristors affect the switching behavior and other properties of electronic devices. Selected memristors will be tested as hardware components for information processing. We hypothesize that long-range order in peptoid nanostructures and the tunability of the macromolecular assemblies will provide new pathways for preparing bio-realistic synaptic node devices and energy-efficient neuromorphic computing network systems.