Скачать или смотреть Materials Project Seminar Series Episode 4: Dr. Kamal Choudhary

Deep Learning and Quantum Computation Methods for Improved Materials Design
Abstract: 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 upto 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/).
Bio: Kamal Choudhary is a research scientist in the Materials measurement laboratory at the National Institute of Standards and Technology (NIST), Maryland, USA and Theiss Research, La Jolla, CA, USA. He received his PhD from University of Florida in 2015 and then joined NIST. His research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, he has developed the JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties. He has published more than 50 research articles in various reputed journals and is an active member of TMS, APS, and MRS societies.