About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Emerging Materials
|
Presentation Title |
Predicting Organic Ligands Mechanical Behavior with Deep Neural Network and Understanding the Mechanism |
Author(s) |
Weiyi Zhang, Chengxi Yang, Alan Fern, Matthew Campbell, P. Alex Greaney |
On-Site Speaker (Planned) |
Weiyi Zhang |
Abstract Scope |
We have developed an automated system for designing organic ligands computationally with a decision tree. This approach formalizes a design space of molecules that is astronomically large and beyond brute force exploration by computer. In order to accelerate the search of this space we have tested machine learning methods for forecasting several different forms of the lingands‘ mechanical and kinematic behavior from four different methods for representing the molecules’ structure. Success at prediction has provided an opportunity to learn the structure-property relationships that give rise to specific dynamic behaviors such as mechano-isomerization and high stiffness. This was achieved by examining the gradients in the response of trained neural networks in order to identify the elements of structure that have the strongest correlation with the property of interest. The results present a formal approach for learning structure-property mechanisms in molecular systems such as metal organic frameworks. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |