-
Notifications
You must be signed in to change notification settings - Fork 77
Workshop Schedule
| | | | :-----|:---- |:------|:------ 9:30 | Intro | Wheeler | 223/A322 9:35 | MGI | Warren | 223/A322 10:00 | Materials Informatics and MKS overview | Fast | 223/A322 11:00 | Lightning talks | Participants | 223/A322 12:30 | Lunch | | 14:00 | MKS Intro Tutorial in Python | Wheeler | 223/A322 16:30 | Code Sprint, PyMKS or other suggested topic | | 223/A322
| | | |
:-----------|:-------------|:--------|:------- 9:30 | Microstructure Informatics ... | Kalidindi | 222/A318 10:30 | Spatial Statistics in Matlab I | Fast | 223/A322 12:30 | Lunch | | 223/A322 14:00 | Advanced Signal Processing for Materials Informatics | Fast | 223/A322 16:30 | Spatial Statistics in Matlab II | Fast | 223/A322 17:30 | Close | | 223/A322
The Plastic Deformation of the TRIP Steels - Sheng Yen Li
Segmentation of Microstructure Images - Gunay Dogan
Prospects for GitHub collaborative code development at NIST - Jonathan Guyer
Workflow Tools: Why I selected IPython Notebook and IPython Cluster - Zachary Trautt
Prospects for MKS methods in OOF - Andrew Reid
Crystal Plasticity Finite Element Analysis - Li Ma
What Constitutes Reproducible Research? - Daniel Wheeler
Materials with enhanced performance characteristics have served as critical enablers for the successful development of advanced technologies throughout human history, and have contributed immensely to the prosperity and well-being of various nations. Although the core connections between the material’s internal structure (i.e. microstructure), its evolution through various manufacturing processes, and its macroscale properties (or performance characteristics) in service are widely acknowledged to exist, establishing this fundamental knowledge base has proven effort-intensive, slow, and very expensive for a number of candidate material systems being explored for advanced technology applications. It is anticipated that the multi-functional performance characteristics of a material are likely to be controlled by a relatively small number of salient features in its microstructure. However, cost-effective validated protocols do not yet exist for fast identification of these salient features and establishment of the desired core knowledge needed for the accelerated design, manufacture and deployment of new materials in advanced technologies. The main impediment arises from lack of a broadly accepted framework for a rigorous quantification of the material’s microstructure, and objective (automated) identification of the salient features in the microstructure that control the properties of interest.
Microstructure Informatics focuses on the development of data science algorithms and computationally efficient protocols capable of mining the essential linkages in large microstructure datasets (both experimental and modeling), and building robust knowledge systems that can be readily accessed, searched, and shared by the broader community. Given the nature of the challenges faced in the design and manufacture of new advanced, this new emerging interdisciplinary field is ideally positioned to produce a major transformation in the current practices used by materials scientists and engineers. The novel data science tools produced by this emerging field promise to significantly accelerate the design and development of new advanced materials through their increased efficacy in gleaning and blending the disparate knowledge and insights hidden in “big data” gathered from multiple sources (including both experiments and simulations). Our research has outlined a specific strategy (i.e. workflow) for data science enabled development of the materials genome, and key components of the proposed overall strategy are illustrated with examples.