About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Machine Learning from Large and Sparse Data for Novel Materials Discovery |
Author(s) |
Fadwa El-Mellouhi |
On-Site Speaker (Planned) |
Fadwa El-Mellouhi |
Abstract Scope |
Materials discovery has prospects of significant acceleration in the upcoming years thanks to the adaptation of data-science and machine learning techniques. In this talk, I will give an overview of the latest advances in this field with focus on materials for energy and environmental applications. I will show how large and sparce datasets constructed from density functional theory (DFT) calculations or experiments can be used to perform a systematic analysis of structure-to-property relationships. Focusing on the correlations between the structural deformations and the thermodynamic stability of compounds, various machine learning algorithms were trained then tested. I will also highlight how our approach offers an interesting guideline on how to engineer novel materials compositions enabling to reduce the huge space of experimental trial and error. |
Proceedings Inclusion? |
Undecided |