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Meeting 2023 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Materials
Presentation Title Computational Design for Metallic Meso-architected Materials for Dynamics
Author(s) H Alicia Kim, Brianna McNider, Ryan Fancher, Po-Shun Chiu, Jaeyub Hyun, Nicholas Boechler
On-Site Speaker (Planned) H Alicia Kim
Abstract Scope We present our latest research in formulating a computational design strategy for mitigating damage resulting from high-strain rate loading events. It is well-known that unprecedented properties can be achieved by tailoring nonlinearities derived from designed mesostructure. Several examples of materials with designed nonlinearity have shown outstanding promise for shock and impact mitigation. Yet, the examples of mesostructure-derived effective material nonlinearities are few and far between. Two primary challenges exist: (1) nonlinearity dramatically expands this unintuitive design space compared to linear material problems, introducing a plague of local minima and non-uniqueness; (2) even if we had an answer to (1), it is not guaranteed that the best nonlinear material can be manufactured. The researchers of the Center for DREAMS (Dynamically Responsive Emergent Architected Material Systems) in the UC system develop a computational approach via mathematical optimization to readily discover the mesostructures of architected materials with desired effective nonlinearity.
Proceedings Inclusion? Planned:
Keywords ICME, Modeling and Simulation, Additive Manufacturing

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Adaptive Discovery and Mixed-variable Bayesian Optimization of Next Generation Synthesizable Microelectronic Materials
An Inverse Materials Design Route Based on Structure-property Linkages Leveraging 3D Convolutional Neural Network and Bayesian Optimization
Applying Data-driven Models in Materials Science: Unraveling Hidden Relationships between Structures and Properties
Atomistic Modeling of Electronic Transport and Electrochemistry
Band Gap Renormalization in 2D Materials from First-principles
Bridging First-principles Calculations with Experiment: Insights from Case Studies on (Photo)Electrochemical Systems
Closed Loop Computational Materials Discovery
Computation Discovery of Materials for Solid-state Batteries
Computational Design for Metallic Meso-architected Materials for Dynamics
Computer Vision Problems in Transmission Electron Microscopy
Crystal to PNG (xtal2png): A Screening Tool to Accelerate Domain Transfer from State-of-the-art Image-processing Models to Materials Informatics and a Case Study on Denoising Diffusion Probabilistic Models
Data- and Physics-driven Approaches to Discovering the Governing Equations for Complex Phenomena in Heterogeneous Materials
Design and Development of High Strength High Conductivity Alloys using ICMDŽ Approach
Design of Bistable Metamaterials for Desired Dynamic Behavior
Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
Designing Ohmic and Schottky Interfaces for Oxide Electronics
Developing an Ab Initio-Kinetic Passivation Model for High-throughput Screening of Material Stability
Electronic and Structural Properties of Ab-initio Predicted BxAl1-xN Alloy Structures
Elucidating the Mechanisms for Fast Diffusion in Doped LLZO
Exploiting First-principles Based Interpretation of X-ray Absorption Spectra of Ni, Cr, Fe Elements in Molten-salt System
Graph Mining in Materials Science for the Prediction of Material Properties
M-16: Building an ImageNet for Materials Grain Boundaries
M-17: Generative Adversarial Networks and Diffusion Models in Material Discovery
M-28: Molecular Dynamics Investigation of Electrochemical Systems
Machine-learning-boosted Searching and Optimization of Emergent Quantum Materials
Machine Learning Assisted Discovery of Composite Solid-state Electrolytes in Context of Li-ion Batteries
Modeling of Local Lattice Distortion Effects on Vacancy Migrations in Multicomponent FCC Alloys
Searching for New "Quantum Defects" through High-throughput Computational Screening
Ultra-fast Interpretable Machine-learning Potentials for Accelerated Structure Prediction of Materials
What is a Minimal Working Example for a Self-driving Laboratory?
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