|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||Uncertainty Quantification for Machine Learning Methods Applied to Material Properties
||Kamal Choudhary, Francesca Tavazza
|On-Site Speaker (Planned)
Next generation material discovery and characterization is heavily dependent on the use of machine learning (ML) approaches. The key ingredients in most ML models are well curated data, descriptors and appropriate algorithms. As ML in materials become more popular, it is essential to quantify uncertainty in terms of descriptors and ML algorithms. In this work, we compare several descriptors and ML algorithms for DFT generated data for molecules, 3D and 2D solid materials. Some of the investigated material properties are formation energy, bandgap, refractive index, elastic constants, topological spillage, and exfoliation energy.
||Planned: Supplemental Proceedings volume