Congratulations to Manas Likhit Holekevi Chandrappa, Ji Qi, Chi Chen and Swastika Banerjee on the publication of “Thermodynamics and Kinetics of the Cathode−Electrolyte Interface in All-Solid-State Li−S Batteries” in the Journal of the American Chemical Society! Lithium−sulfur batteries (LSBs) use cheap and abundant sulfur in place of expensive metal-based cathodes. Using a solid electrolyte in place of traditional liquid electrolytes mitigates polysulfide shuttling, a key impediment to LSB commercialization. In this work, we present a comprehensive study of the thermodynamics and kinetics of the cathode−electrolyte interface in all-solid-state LSBs. Using DFT calculations, we show that among the major solid electrolyte chemistries (oxides, sulfides, nitrides, and halides), sulfides are the most stable solid electrolytes against the S cathode, as well as the most promising buffer layers if the use of other SE chemistries is desired. Finally, MD simulations with an accurate machine learning interatomic potential revealed that the most stable Li3PS4(100)/S interfaces form 2D channels with lower activation barriers for Li diffusion. These results provide critical new insights into the cathode−electrolyte interface design for next-generation all-solid-state LSBs. We gratefully acknowledge Nissan Motor Inc and Nissan North America for their generous support for this work! This work has been published open access […]
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 […]
Our collaborative paper with the Ritchie and Asta groups on “Atomistic simulations of dislocation mobility in refractory high-entropy alloys (RHEAs) and the effect of chemical short-range order” has been published in Nature Communications! RHEAs are designed for high elevated-temperature strength, with both edge and screw dislocations playing an important role in plastic deformation. Using the highly accurate machine learning interatomic potential developed by MAVRL alum Dr Yunxing Zuo, we investigate mechanisms underlying the mobilities of screw and edge dislocations in the bcc MoNbTaW RHEA over a wide temperature range using MD simulations, and how these mechanisms are affected by the presence of short range order. We show that the mobility of edge dislocations is enhanced by SRO, while the rate of double-kink nucleation in the motion of screw dislocations is reduced. We also found a cross-slip locking mechanism for the motion of screws, which provides for extra strengthening for bcc RHEAs. Check out this work at this link.