| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Case Studies: Successes and Challenges for Generation, Distribution, and Use of Public/Pre-Existing Materials Datasets
|
| Presentation Title |
Holistic Merging of Experimental and Computational Datasets – A Case Study for Diffusion Coefficients |
| Author(s) |
Wei Zhong, Ji-Cheng Zhao |
| On-Site Speaker (Planned) |
Ji-Cheng Zhao |
| Abstract Scope |
It is usually challenging to reconcile the differences between the computational datasets and experimental datasets when merging them together. Fortunately for some materials properties, large amounts of data are available to allow identification of the degree of agreements and disagreements, and thus give confidence on some or all aspects of the computational datasets. Diffusion coefficients are one of such cases where holistic merging of the computational and experimental datasets are possible to leverage the best of both datasets. Examples will be given to illustrate such holistic integration in order to establish reliable diffusion coefficient (atomic mobility) databases for simulating kinetic processes and properties of alloys. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, ICME, Modeling and Simulation |