ProgramMaster Logo
Conference Tools for 2022 TMS Annual Meeting & Exhibition
Register as a New User
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting 2022 TMS Annual Meeting & Exhibition
Symposium Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
Presentation Title Designing Nano-architectured Materials with a Machine-learning Augmented Framework
Author(s) Chonghang Zhao, Cheng-Chu Chung, Yu-chen Karen Chen-Wiegart
On-Site Speaker (Planned) Yu-chen Karen Chen-Wiegart
Abstract Scope Design parameter space for nano-architectured materials is large and complex. Even with the guidance of simulation and theoretical computation, creating nano-architectured materials, despite being at the forefront of materials science, could suffer from the common issue of intuition-driven “trial-and-error” materials discovery. While there has been a significant increase in developing data-driven, machine-learning (ML) based methods for efficient materials design, only recently have researchers begun to integrate these approaches with advanced, high-throughput characterization tools. This talk aims to provide an overview on the latest development of this exciting direction in materials science and engineering. A use case of solid-state interfacial dealloying (SSID) creating bicontinuous nanostructures will also be presented; the example will highlight the use of ML-based models and algorithms to predict materials’ synthesibility as well as the promise to apply automated and autonomous experimental approaches at synchrotron facilities to further enhance and validate the ML models.
Proceedings Inclusion? Undecided
Keywords Machine Learning, Nanotechnology, Thin Films and Interfaces


Accelerated Discovery of Multi-phase Refractory Alloys through Machine Learning Surrogate Models of CALPHAD
Autonomous X-ray Scattering for the Study of Non-equilibrium Self-assembly
Designing Nano-architectured Materials with a Machine-learning Augmented Framework
Discovery of Nanocomposite Phase Change Memory Materials via Closed-loop Autonomous Combinatorial Experimentation
Intelligent Design of Additively Manufactured Architected Materials
Machine Learning Based Hierarchical Multi-scale Modeling of Mechanical Deformation for Metal-matrix-nano-composites
Volumetric Nanoscale Imaging of DNA-assembled Nanoparticle Superlattices
“Big Data” Characterization of Material Properties and High Temperature Kinetics

Questions about ProgramMaster? Contact