Materials Virtual Lab

Graph networks are an extremely powerful deep learning tool for predicting materials properties. However, a critical weakness is their reliance on large quantities of training data. In this work published in npj Computational Materials, Dr Chi Chen shows that pre-trained MEGNet formation energy models can be effectively used as “encoders” for crystals in what we call the AtomSets framework. The compositional and structural descriptors extracted from graph network deep learning models, combined with standard artificial neural network models, can achieve lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits. AtomSets also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits, require minimal domain knowledge inputs and are free from feature engineering.

Check out this work here.

Our paper on “Accelerating materials discovery with Bayesian optimization and graph deep learning” has just been published in Materials Today! In our group, we are firm advocates of ML models that utilize structure-based features, because only such models can reliably predict property differences between chemically similar but structurally different materials (e.g., diamond vs graphite). However, a bottleneck remains in that obtaining an input structure today still depends on expensive DFT calculations. Here, we show that Bayesian optimization with an accurate MEGNet energy model can be used to obtain sufficiently good input structures for ML model predictions. We demonstrated the power of this approach by screening 400,000 materials for ultra-incompressibility. Two completely novel materials are realized experimentally by Mingde Qin in Prof Jian Luo’s group at UCSD. This work paves the way to ML-accelerated discovery of new materials with exceptional properties.

Check out this work here.

Congratulations to Ji Qi on his paper on “Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors” published in Materials Today Physics. Part of a “Special Issue on Solid state batteries: materials, characterizations, and understandings”, this is Ji’s first first-author paper. Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. However, ab initio molecular dynamics (AIMD) often make wrong predictions of their ionic conductivities due to the short time scales and small cell sizes used. Here, we present a strategy to bridge this gap using machine learning interatomic potentials based on the moment tensor potential (MTP) formalism. We show that the DFT functional used to train the MTPs plays a critical role in the accuracy of the predictions. In particular, the van der Waals optB88 functional yield much more accurate lattice parameters, which in turn leads to accurate prediction of ionic conductivities and activation energies for Li0.33La0.56TiO3, Li3YCl6 and Li7P3S11. Nanosecond NPT MD simulations also reveal that all three lithium superionic conductors undergo a transition between two quasi-linear Arrhenius regimes at relatively low temperatures. This transition can be traced to an increase in the number and diversity of diffusion pathways, in some cases with a change in the dimensionality of diffusion. This work presents not only an approach to develop high accuracy MTPs, but also outlines the diffusion characteristics for LSCs which are otherwise inaccessible through ab initio computation.

Check out the work at this link.

Yiming’s paper on “Database of ab initio L-edge X-ray absorption near edge structure” has just been published in Nature Scientific Data! This work is a collaboration between the Materials Virtual Lab, the Materials Project, Alan Dozier and the groups of Prof John Rehr at the University of Washington and Prof Jordi Cabana at the University of Illinois Chicago. It is a follow-up to our earlier work on a K-edge XANES database, the L-edge XANES database provides instant access to more than 140,000 L-edge spectra for more than 22,000 structures generated using a high-throughput FEFF9 workflow. The L-edge XANES is widely used in the characterization of transition metal compounds. The data is available through the Materials Project XAS app and addresses a critical need for L-edge XANES spectra among the research community. The journal article is available at this link.

Manas just published his first paper on “Correlated Octahedral Rotation and Organic Cation Reorientation Assist Halide Ion Migration in Lead Halide Perovskites” in Chemistry of Materials! Halide ion migration is one of the main contributors to instability and hysteresis in lead halide perovskite (LHP) solar cells. In this collaborative work with the Fenning group, we elucidate the effect of octahedral rotation and organic cation rotation on halide ion migration in APbBr3 (A = Cs or methylammonium/MA) LHPs. While both effects lower halide migration barriers, organic cation rotation plays a much bigger role in hybrid organic-inorganic LHPs, which can be linked to changes in H bonding during the halide migration process. We suggest that “locking” the organic cation via chemical and processing means can help mitigate halide migration-induced instability and reduced hysteresis in LHP solar cells.

Check out the work at this link.