Predictive Modeling of Polymer-Derived Ceramics: Discovering Methods for the Design and Fabrication of Complex Disordered Solids Paul Rulis1 , Nathan Oyler2 , Michelle Paquette1, Ridwan Sakidja3, Jinwoo Hwang4 1. Department of Physics and Astronomy, University of Missouri - Kansas City, Kansas City MO 64110 2. Department of Chemistry, University of Missouri - Kansas City, Kansas City MO 64110 3. Department of Physics, Astronomy and Materials Science, Missouri State University, Springfield MO 65897 4. Department of Materials Science and Engineering, The Ohio State University, Columbus OH 43212 Program Objective • Toward the long-term goal of creating an integrated program for the design of complex disordered solids, this new project aims to develop a general, simulation- driven methodology for accurately recreating the atomic structure of substructure- containing amorphous solids and mapping resultant structures and properties back to fabrication conditions, ultimately enabling a computational design capability. • The work involves developing an ab initio molecular dynamics (AIMD) and hybrid reverse Monte Carlo (HRMC) simulation that is augmented by ab initio total energy and a set of geometric constraints. We will use a collection of thin-film amorphous preceramic polymers (a-BC:H, a-SiBCN:H, and a-SiCO:H) as suitably complex and technologically relevant case studies. • We develop novel algorithms for linking growth conditions and characterization information to atomistic simulation, and for mapping fabrication conditions to desired properties. Our modeling goal is to first identify non-global potential energy minima for material sub-components that are produced under non-thermodynamic conditions and to then align the timescales of the simulation and growth processes so as to generate realistic structural models of complex disordered solids. • The project combines state of the art computational techniques (AIMD, HRMC), modern optimization algorithms (e.g. artificial neural networks (ANNs), particle swarm optimization (PSO)), specialized experimental characterization techniques, (solid-state nuclear magnetic resonance (NMR), 4-dimensional scanning transmission electron microscopy (4D-STEM)), and advanced thin-film fabrication technology (plasma enhanced chemical vapor deposition (PECVD)). • The unique utility of modern solid-state NMR techniques to obtain specific bonding and connectivity information and the sensitive medium-range order information available from 4D-STEM will be combined with neutron diffraction and more routine physical/electronic structure characterization methods to provide input and constraints for the simulations. The project includes (left) growth and characterization, (middle) iterative modeling, and (right) design training and validation. Single-frame red boxes represent experimental samples and data, while double-framed blue boxes represent computational products. The shaded region in the middle represents the application of particle swarm optimization. The general flow can be understood as: (1) growth of samples varied by composition and growth procedures; (2) experimental structural characterization; (3) iterative model simulation using characterization data; (4) ANN training to link simulation and growth parameters followed by predictive application of the ANN. Acknowledgement To meet the long-term goal of developing an integrated program for the fabrication of complex disordered solids that are designed to satisfy the performance requirements of advanced applications, we set the following plans for the first year of this project. 1. We will form connections between experimental characterization and simulated data to guide the construction of realistic models of preceramic polymers. Our working procedure for generating individual models is to prepare the composition for a large-scale classical MD simulation via smaller scale AIMD and to then use HRMC to progressively improve matching between the mod