This project focusses on computational modeling of sustainable organic and bio-based materials for next-generation energy storage, bioelectronics, and renewable material applications. Our work integrates quantum mechanical calculations, molecular dynamics simulations, and AI/ML-driven modeling to bridge molecular-level interactions with device-scale behavior. The project focuses on two primary research directions: 1. Mixed Ionic-Electronic Conducting Polymers (MIECPs): MIECPs are soft, flexible, and biocompatible materials capable of transporting both ions and electrons, making them ideal for emerging technologies such as printed electronics, neuromorphic systems, implantable bioelectronics, and sustainable energy storage devices. Despite their technological importance, progress is limited by a lack of fundamental theoretical understanding of key properties such as intrinsic capacitance, density of states (DOS), and charge transport mechanisms. We will develop an AI-powered, multi-scale computational framework to predict these properties and link molecular-scale phenomena to macroscopic device performance. This effort involves hybrid quantum-classical (QM/MM) simulations, coarse-grained molecular dynamics, and machine learning models trained on large datasets generated through HPC calculations. The framework will provide a predictive toolset for designing high-performance MIECPs and resolving long-standing questions about their electrochemical processes. 2. Lignin Molecular Modeling and Nanoparticle Engineering: Lignin, the most abundant natural aromatic polymer, is a largely underutilized byproduct of the pulp and paper industry, with most of it currently burned for energy recovery. By leveraging advanced simulations, we aim to transform lignin into a high-value, renewable resource. Using molecular modeling, we will develop predictive coarse-grained and supra-coarse-grained models to understand lignin’s complex structure, self-assembly, and the formation of lignin nanoparticles (LNPs). These simulations will provide mechanistic insight into LNP formation and guide scalable production processes. Applications include sustainable emulsifiers, pharmaceutical formulations, and additives for green cement, supporting the transition to a circular, fossil-free bioeconomy. Computational Requirements and Methodology: The project employs a broad suite of HPC-intensive codes, including Gaussian, VASP, GAMESS-US, GROMACS, LAMMPS, CP2K, Quantum Espresso, and COMSOL Multiphysics, alongside visualization tools such as VMD and GaussView. Simulations are fully parallelized and regularly optimized through performance/scalability tests to ensure efficient use of computing resources. Hybrid QM/MM calculations, machine learning training, and large-scale coarse-grained simulations are highly computationally demanding. With two new team members joining in 2025–2026 and a significant expansion in project scope, our current allocation of 400,000 core-hours per month is insufficient to meet demand. We therefore request an increase to 600,000 core-hours per month on Tetralith @ NSC, along with consolidation of all resources at NSC for improved workflow and data management efficiency. By combining cutting-edge computation, AI-driven modeling, and close experimental collaboration, this project will generate new knowledge and predictive tools for sustainable materials. NAISS resources are critical to achieving these goals, enabling simulations at the scale and complexity required to address fundamental scientific questions and drive innovation in energy, electronics, and the bioeconomy.