Yunxing gave a talk at NanoHUB’s Hands-on Data Science and Machine Learning Training Series today on how to conveniently develop machine learning interatomic potentials (ML-IAPs) using the Materials Machine Learning (maml) library. ML-IAPs describe the potential energy surface using local environment descriptors and has been demonstrated to be able to achieve near-DFT accuracy with linear scaling with respect to the number of atoms. The recording of this talk is now available on the Materials Virtual Lab’s Youtube channel. To find out more about the maml package, check out our Github repository. You can also read Yunxing’s excellent paper benchmarking the performance and cost of various ML-IAPs to learn more.
Our paper on “Learning properties of ordered and disordered materials from multi-fidelity data” has just been published in the inaugural issue of Nature Computational Science! In this work, we address two major impediments to ML for materials science. The first impediment is that valuable accurate data is much more expensive to obtain than less accurate data. Using multi-fidelity materials graph networks (MEGNet), we show that we can use the lower quality data to improve underlying structural representations in models, and in the process significantly improve predictions on smaller, more valuable data (e.g., experimental measurements). The second impediment is that making predictions on disordered materials, which is the vast majority of known materials, is much more difficult than on ordered materials. We show that the elemental representations (embeddings) learned by our MEGNet models can be used to directly model disordered materials. The article is available here. For an independent perspective on the findings, check out the Nature News & Views article. All data and code are available from http://crystals.ai and the Github repository.
Zhuoying’s paper on “Design Principles for Cation-Mixed Sodium Solid Electrolytes” is the first publication from the Materials Virtual Lab in 2021! All-solid-state sodium-ion batteries are highly promising for next-generation grid energy storage with improved safety. Published in Advanced Energy Materials, this work develops design rules for the highly promising family of cation-mixed Na superionic conductors Na3PnS4-Na4TtS4. We show that cation mixing results in the “worst of both worlds” in terms of electrochemical stability, but can potentially lead to improved ionic conductivity and moisture stability. In particular, the recently reported Na11Sn2PnS12 superionic conductors are shown to be stable and Na11Sn2AsS12 is identified as a hitherto unexplored stable sodium superionic conductor with higher Na + conductivity and better moisture stability than those already reported experimentally. Check out the work here.