Hello, A new project on the LCRC cluster has been requested. Please forward the information on to the LCRC Allocation sub-committee. Applicant's name: Angel Yanguas-Gil Applicant's institution: ANL Applicant's division: ES Project Name: madman Project title: Modeling the advanced manufacturing of energy materials Modeling the advanced manufacturing of energy materials Modeling the advanced manufacturing of energy materials Associated funding: This effort is supported through LDRD and EERE funds Other Systems: None Science: This project aims at developing multiscale predictive models for advanced manufacturing applications, specifically nanomanufacturing and additive manufacturing. Our research has two goals: 1) improve the current understanding of the fundamental processes controlling the synthesis and microstructure of materials, 2) develop models that can predict the scale up of manufacturing process as well as the performance of the resulting materials. Project description: Confidence on the interplay between synthesis, microstructure, and materials performance and reliability is crucial for the adoption of advanced manufacturing techniques at an industrial scale. One of the challenges of novel advanced manufacturing techniques is that by departing from traditional materials and synthesis techniques, the interplay between these three elements is not well understood. This compromises our ability to understand and predict the scalability of the process and long-term reliability of the materials. The goal of this project is to develop models that help us strengthen our understanding on the impact of synthesis conditions on the microstructure and properties of the material. In this project we are going to focus on processes that integrate gas phase synthesis techniques, such as Atomic Layer Deposition, and MOCVD, with other processes such as 3D printing or nanopatterning and etching to achieve novel architectures and materials. These processes are at the core of two SI-LDRDs and one EERE-funded project, and they require the integration of processes taking place at multiple length and timescales. Our current approach uses the following tools: - OpenFOAM: we have developed solvers based on OpenFOAM that bridge the reactor-scale with feature scale to study the synthesis of high surface area nanomaterials and 3D substrates using ALD and CVD. These models have been fully tested and validated in our lab, and the scalability of OpenFOAM has been well established. - LAMMPS: for atomistic simulations of bulk and surfaces. We use LAMMPS to understand the microstructure and stability of the resulting materials and calculate some of the input data required for other models. - Kinetic Monte Carlo simulations: we are focusing o approaches to simulate materials growth that accelerate the speed of calculations by focusing on the growth timescales and treating faster processes under a quasiequilibrium approximation. This acceleration in terms of timescale comes at the expense of a greater computational expense of each Monte Carlo step. However, such cost can be distributed more efficiently among multiple processors and cores. The time allocated for this proposal will be initially devoted to demostrate the ability to efficiently run our models in LCRC's clusters and benchmark the scale up of our simulations. We will then focus on solving larger scale problems that are not feasible in our 8-core workstations. Industry partnership: Project URL: Requested allocation: 60000 Q1: 0 Q2: 10000 Q3: 20000 Q4: 30000 Justification: Storage requirements: The requester has used undetermined amount hours of their initial startup project. In addition to approving an initial amount, please specify a Category and Subcategory for this project. For a list of the current selection of approved categories, please see: https://wiki.lcrc.anl.gov/wiki/Processes/Categories Once the Allocation committee has approved the project, please go to the Project Management page to create it: https://accounts.lcrc.anl.gov/projects.php Thank You, The LCRC Accounts System