Kamal Choudhary, Scientist and Founder of JARVIS, NIST and Theiss Research
Wednesday October 13th, 10am (USA/Pacific)
In this talk, we’ll discuss deep learning methods 1) Graph neural network (GNN) for improved atomistic material property predictions of solids and molecules, 2) Convolutional neural network for STM and STEM image related tasks, and quantum algorithm method 3) Variational Quantum Eigensolver (VQE) for predicting electron and phonon properties. Many GNN models for atomistic property predictions are based on bond-distances mainly. We developed Atomistic Line Graph Neural Network (ALIGNN) that performs message passing on both the bond-distances as well as bond-angles. We apply ALIGNN to train 52 models for properties in the Materials Project, JARVIS-DFT and QM9 datasets leading to up to 85 % improved performance compared to previously known GNN methods. Next, we’ll discuss the AtomVision package which can be used to generate scanning tunneling microscope (STM) and scanning transmission electron microscope (STEM) datasets. Then we apply deep learning frameworks for image classification and defects detection tasks for 2D materials. Currently, the application of quantum algorithms such as VQE is mainly limited to molecules. We’ll show using tight-binding approaches for electrons and phonons, quantum circuit-based methods can be applied for solids also. All of the above projects are part of the NIST-JARVIS infrastructure (https://jarvis.nist.gov/).
A recording of this seminar will be available after the talk.
If you are unable to ask questions live, please feel welcome to ask any questions following the talk here and we will ask the speaker to check afterwards. Whether they will be able to answer questions or not depends on the speaker’s availability.