| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Prediction of Formable Apatites using Machine Learning and Density Functional Theory |
| Author(s) |
Timothy Q. Hartnett, Mukil Ayyasamy, Prasanna Balachandran |
| On-Site Speaker (Planned) |
Timothy Q. Hartnett |
| Abstract Scope |
Materials with apatite structure have applications ranging from biomaterials to fuel cells. Their chemical flexibility and structural diversity provide a fertile ground to tune functionalities as potential candidates for many applications. In this work, Random Forest and Gradient Tree Boosting methods are applied to search for formable compounds in the apatite structure-type. The results are compared with a traditional convex hull analysis of formation energy predicted from density functional theory (DFT) calculations. We assert that data-driven methods have the potential to capture more formable compounds than the hull method since these are based on experimental results rather than DFT simulations. By comparing formable apatites to their predicted hull energies we are able to derive a “Degree of Metastabilty” where the Hull analysis predicts the compound is unable to form but formation is still observed. Thus, using machine learning, we are able to identify compounds which would be missed by DFT. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |