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 […]
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.