Kamal Choudhary¹, Francesca Tavazza²; ¹University of Maryland(National Institute of Standards and Technology, ²Umcp/Nist

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.