Month: July 2022

Publications

CdS/V3O5 for Neuromorphic Computing

Congratulations to Jasleen on her first co-author paper on “An Optoelectronic Heterostructure for Neuromorphic Computing: CdS/V3O5” in Applied Physics Letters! Nonvolatile resistive switching is one of the key phenomena for emerging applications in optoelectronics and neuromorphic computing. However, the stochastic nature of the ion migration can be an impediment for the device robustness and controllability, with uncontrolled variations of high and low resistance states or threshold voltages. In this work, we report an optically induced resistive switching based on a CdS/V3O5 heterostructure. V3O5 is known to have a second order insulator to metal transition around 415 K, with an electrically induced threshold switching at room temperature. Upon illumination, the direct transfer of the photoinduced carriers from the CdS into V3O5 produces a nonvolatile resistive switching at room temperature. Jasleen’s contribution is in using DFT calculations to understand the defects present in V3O5 and the effects of electron doping. We show that electrons (generated by CdS under illumination) injected in V3O5 are trapped in a deep state, slowing the “low” temperature relaxation rate. For the LT phase (T < 340 K), the photoexcited electrons trapped into the oxygen vacancy are unable to overcome the barrier, and therefore, no relaxation is observed. […]

Presentations

CCMS Summer Institute Lecture 2022

Graphs are a natural way to represent atoms and bonds. In this lecture titled “Mathematical Graphs as a Representation for Materials”, Prof Shyue Ping Ong introduces the basics of graph deep learning and its application in materials science. MatErials Graph Networks (MEGNet) models have immense flexibility and expressiveness that can be adapted to datasets of diverse quality and quantity. We also demonstrate how the application of simple principles like energy minimization or interatomic development with materials graph models with 3-body interactions (M3GNet) can be used in the discovery of new materials **without** ab initio calculations, paving the way for massive-scale computational materials design. Prof Ong also introduces the matterverse.ai initiative, an open initiative to use ML to greatly expand the explorable matterverse. This lecture also includes two hands-on tutorials using Google Colab to demonstrate key concepts and the application of MEGNet and M3GNet models for property predictions and crystal structure relaxation. This Lecture is part of the Lawrence Livermore National Laboratory (LLNL) Computational Chemistry & Materials Science (CCMS) Summer Institute held from June 6 to August 12, 2022. The program offers graduate students the opportunity to work directly with leading LLNL researchers on the development and application of cutting-edge methods […]