ProgramMaster Logo
Conference Tools for 2023 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 2023 TMS Annual Meeting & Exhibition
Symposium Accelerated Discovery and Insertion of Next Generation Structural Materials
Presentation Title AI and Machine Learning Tools for Development and Analysis of Image Driven 2D Materials
Author(s) Hafiz Munsub Ali, Venkata A. S. Kandadai, Bharat K Jasthi, Venkataramana Gadhamshetty, Etienne Z Gnimpieba
On-Site Speaker (Planned) Hafiz Munsub Ali
Abstract Scope Recent advances in data driven computing leverage artificial intelligence and machine learning techniques to solve diverse challenges involved 2D material. Microscopic and spectroscopic data are involved in complete life cycle of 2D material such as design, discovery. characterization, maintenance, etc. This review uses PRISMA as a systemic review process to ensure relevance and reproducibility. Articles are searched in three publication databases (WoS, PubMed, Dimensions) with two key research questions. PRISMA identifies 31 articles relevant to the topic. The most studied questions are 2D new material synthesis and engineering with reasonable appreciation for functional discovery/property, defect characterization and grain boundary, but work on corrosion application/detection is not done yet as per our analysis. From the relevant set of articles, the tasks prediction, classification, deep learning, clustering, and AI appeared 38.98%, 28.81%, 13.56%, 10.17%, and 8.47% respectively. Results show that AI (i.e., 8.47%) is still an underexplored computing technique.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Copper / Nickel / Cobalt, Characterization


A Design Space for Tunable Ceramic-polymer Composites
A Diffusion Couple Approach to β-Ti Alloy Development: Evaluating the Oxidation Performance of Ti-Fe-X+ Alloys
A High-throughput Setup for Materials Exposure to Simultaneous Irradiation-corrosion Conditions
Accelerated Discovery of Novel Titanium Alloys using High-throughput Manufacturing, Characterization and Testing
Accelerating Multimodal Data Collection: A Workflow for Metallic Films
AI and Machine Learning Tools for Development and Analysis of Image Driven 2D Materials
Combinatorial Mechanical Microscopy via Correlated Nanoindentation and EDX Mapping
Computational Design of an Ultra-strong High-entropy Alloy
Computational Design of High Entropy Alloy Hardmetals
Design of a Compact Morphology Cobalt-based Superalloy for Additive Manufacturing
Efficient Conductivity and Hardness Optimization in Cu-Ag-Ni Alloys using Bayesian Active Learning
High-throughput Electric-Field-assisted Sintering and Characterization Techniques for Materials Discovery
High-throughput Prediction of Fracture and Brittle to Ductile Transition in Tungsten using Variable Temperature Nanoindentation
High-throughput Synthesis and Mechanical Characterization of Sputtered Metallic Alloys
How Should You Select an Algorithm for a Materials Discovery Campaign with Multiple Objectives, Complex and High-dimensional Structure-processing-property Relationships, and a Small Adaptive Design Budget?
Machine Learning-assisted Discovery of Novel High Temperature Ni-rich NiTiHfZr Multi-component Shape Memory Alloys
Rapid Characterisation of Active Slip Systems in Titanium Ordered-bcc Compounds using an Algorithm for Automated Indentation Slip Trace Analysis.
Using Machine Intuitive Learning to Predict Advanced Steel Properties

Questions about ProgramMaster? Contact