Why software development is important

Jean Fan’s recent column in Nature on “Why it’s worth making computational methods easy to use” is an excellent article on a topic close to my heart. The following quote rings especially true. “We probably spent as many hours making STdeconvolve accessible as we did in developing it. Some of my colleagues have been surprised by this effort, as those hours won’t lead to new publications.” Our group is the maintainer of pymatgen, maml, matgl and a few other software used extensively by the materials science community. Colleagues frequently asked me the same question – “Why do I do it? Surely a professor can spend the time writing proposals, papers, etc.?” I disagree. Our group’s code is a critical avenue in which we contribute and engage with the community. A well written and maintained code can probably 10-100x the impact of a work beyond that one publication. I argue the Materials Virtual Lab, in open collaboration with thousands of other researchers, have saved millions of hours in research hours because some graduate student or postdoc was able to do their research faster and more accurately. That may not appear in my CV, but it makes me motivated to continue to […]

GB resistance in oxide solid electrolytes

Ji Qi’s co-first author paper on “Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li0.375Sr0.4375Ta0.75Zr0.25O3 (LSTZ0.75)” has been published in Nature Communications! This is a highly-collaborative work under the Center for Complex and Active Materials (CCAM), an NSF MRSEC. Perovskite solid electrolytes for all-solid-state lithium-ion batteries are often plagued by grain boundary (GB) resistance. In this work, the CCAM team use aberration-corrected scanning transmission electron microscopy and spectroscopy, along with an active learning moment tensor potential, to reveal the atomic scale structure and composition of LSTZ0.75 grain boundaries. A key finding is that Li depletion in the GB is mitigated in LSTZ0.75 compared to the typical LLTO peroskite SE. Instead, a nanoscale defective cubic perovskite interfacial structure that contained abundant vacancies is formed. Ji’s contribution is the development of an accurate machine learning interatomic potential to study the highly complex LLTO and LSTZ perovskites, including the GB structures. Using MC and MC simulations, we demonstrate that Li enrichment and Sr vacancies in the GBs of LSTZ play a key role in fast diffusion in LSTZ. Check out this work here.

Polarons in Vanadium Oxides

Jasleen’s first-author paper on “Polaron-induced metal-to-insulator transition in vanadium oxides from density functional theory calculations” is out in Physical Review B! Vanadium oxides are promising phase-change memory units for neuromorphic computing due to their metal-insulator transitions (MIT) at or near room temperature. In this work, we show that V3O5 exhibits very low hole and electron polaron migration barriers (< 100 meV) compared to V2O3 and VO2, leading to much higher estimated polaronic conductivity. The relative migration barriers are found to be related to the amount of distortion that has to travel when the polaron migrate from one site to another. Polarons in V3O5 also have smaller binding energies to vanadium and oxygen vacancy defects. These results explain recent experiment studies showing the injection of charge carriers into vanadium oxides as an alternative switching mechanism and also potentially as a means to tune the MIT temperature. Check out this work here.

The intercalation chemistry of DRX-Li3V2O5 anode

Xingyu’s swansong work in the Materials Virtual Lab, “Intercalation Chemistry of the Disordered Rocksalt Li3V2O5 Anode from Cluster Expansions and Machine Learning Interatomic Potentials” has been published in Chemistry of Materials! We revisited the intercalation chemistry of the highly promising DRX-Li3V2O5 using machine learning-based computational techniques that enable much larger scale simulations. DRX Li3V2O5 is a promising anode candidate for rechargeable lithium-ion batteries because of its low voltage, high rate capability, and good cycling stability. In contrast to previous DFT studies, we show that insertion of Li primarily occurs in the tetrahedral sites and that the voltage profile depends critically on the initial Li/V disorder. MD simulations also show that DRX-Li3V2O5 has a fast Li diffusivity, which depends on the concentration of Li. We propose tuning the Li:V ratio as a means of trading off increased lithiation capacity and decreased anode voltage in this system. This work provides in-depth insights into the high-performance DRX-Li3V2O5 anode and paves the way for the discovery of other disordered anode materials. Check out the work here.

Matterverse.ai and M3GNet Universal IAP

Dr Chi Chen’s swansong work in our group, “A Universal Graph Deep Learning Potential for the Periodic Table” is now published in Nature Computational Science! Interatomic potentials (IAPs), which describe the potential energy surface of atoms, are a fundamental input for atomistic simulations. However, existing IAPs are either fitted to narrow chemistries or too inaccurate for general applications. In this work, we combine graph neural networks with traditional 3-body interactions to develop a flexible, yet accurate architecture for machine learning of materials properties. Using the massive database of structural relaxations performed by the Materials Project over the past ten years, we train a universal IAP for 89 elements of the periodic table with broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces. Using the new capabilities of the M3GNet universal IAP, we are proud to launch matterverse.ai, a ML database of yet-to-be-synthesized materials. Matterverse.ai currently contains about 31 million hypothetical crystal structures, of which about 1.8 million materials were identified to be potentially stable. The database also provides ML properties using state-of-the-art multi-fidelity MEGNet models, such as experimental, HSE and PBE band gaps, bulk and shear moduli, etc. Check out the article here. […]

Interfacial Stability of NMC cathodes

Congrats to Hideyuki Komatsu, our visiting scientist from Nissan, on his first author work “Interfacial Stability of Layered LiNixMnyCo1−x−yO2 Cathodes with Sulfide Solid Electrolytes in All-Solid-State Rechargeable Lithium-Ion Batteries from First-Principles Calculations” published in the Journal of Physical Chemistry C! In this work, we explore the relationship between the composition of layered LiNixMnyCo1−x−yO2 (NMC) cathodes and interfacial stability in all-solid-state lithium-ion batteries. A key insight is that the broader commercial trend towards high Ni content to reduce cost leads to significantly more reactive interfaces with the Li6PS5Cl argyrodite solid electrolyte. This suggests that current efforts to reduce the Co content in cathodes may compromise potential applications in all-solid-state architectures. Nevertheless, we find that common SEI phases such as Li2CO3, surface phases such as NiO, and oxide buffer layers such as LiNbO3 can provide effective protection between NMC and LPSCl. Check out the work here.

Interfacial Stability of Lithium Sulfur Batteries

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 […]

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. […]

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 […]

Universal ML model for GB energies

Weike’s swansong project in our group on “A Universal Machine Learning Model for Elemental Grain Boundary Energies” has just been published in Scripta Materialia! The grain boundary (GB) energy has a profound influence on the grain growth and properties of polycrystalline metals. Here, we show that the energy of a GB, normalized by the bulk cohesive energy, can be described purely by four geometric features to within a mean absolute error of 0.13 Jm-2 . More importantly, this universal GB energy model can be extrapolated to the energies of high Σ GBs without loss in accuracy. These results highlight the importance of capturing fundamental scaling physics and domain knowledge in the design of interpretable, extrapolatable machine learning models for ma­terials science. Check out this work here.