Publications

We show that the deposition of the solid-state electrolyte LiPON onto films of V₂O₅ leads to their uniform lithiation of up to 2.2 Li per V₂O₅, without affecting the Li concentration in the LiPON and its ionic conductivity. Our results indicate that Li incorporation occurs during LiPON deposition, in contrast to earlier mechanisms proposed to explain postdeposition Li transfer between LiPON and LiCoO₂. We use our discovery to demonstrate symmetric thin film batteries with a capacity of >270 mAh/g, at a rate of 20C, and 1600 cycles with only 8.4% loss in capacity. We also show how autolithiation can simplify fabrication of Li iontronic transistors attractive for emerging neuromorphic computing applications. Our discovery that LiPON deposition results in autolithiation of the underlying insertion oxide has the potential to substantially simplify and enhance the fabrication process for thin film solid state Li ion batteries and emerging lithium iontronic neuromorphic computing devices.

Bimetallic transition metal complexes with site-specific redox properties offer a versatile platform for understanding electron polarization, intramolecular electron transfer processes, and customizing electronic and magnetic properties that might impact reactivity and catalyst design. Inspired by the dissymmetric nickel sites in the Acetyl CoA Synthase (ACS) Active Site, three new bimetallic Ni(N₂S₂)–Ni(S₂C₂R₂) complexes based on Ni(N₂S₂) metalloligand donor synthons, Ni_d, in mimicry of the nickel site distal to the redox-active iron sulfur cluster of ACS, and nickel dithiolene receiver units, designated as Ni_p, the nickel proximal to the 4Fe4S cluster, were combined to explore the influence of ligand environment on electronic structure and redox properties of each unit. The combination of synthons gave a matrix of three S-bridged dinickel complexes, characterized by X-ray crystallography, and appropriate spectroscopies. Computational modeling is connected to the electronic characteristics of the nickel donor and receiver units. This study demonstrated the intricacies of identifying sites of electrochemical redox processes, within multi-metallic systems containing non-innocent ligands.

Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. The periodic and infinite nature of crystals poses unique challenges for geometric graph representation learning. Specifically, constructing graphs that effectively capture the complete geometric information of crystals and handle chiral crystals remains an unsolved and challenging problem. In this paper, we introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom, enabling efficient and expressive graph representations of crystals. Furthermore, we propose ComFormer, a SE(3) transformer designed specifically for crystalline materials. ComFormer includes two variants; namely, iComFormer that employs invariant geometric descriptors of Euclidean distances and angles, and eComFormer that utilizes equivariant vector representations. Experimental results demonstrate the state-of-the-art predictive accuracy of ComFormer variants on various tasks across three widely-used crystal benchmarks. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).

In this work, we developed three methods to map crystallographic variants of samples at the nanoscale by analyzing precession electron diffraction data using a high-temperature shape memory alloy and a VO₂ thin film on sapphire as the model systems. The three methods are (I) a user-selecting-reference pattern approach, (II) an algorithm-selecting-reference-pattern approach, and (III) a k-means approach. In the first two approaches, Euclidean distance, Cosine, and Structural Similarity (SSIM) algorithms were assessed for the diffraction pattern similarity quantification. We demonstrated that the Euclidean distance and SSIM methods outperform the Cosine algorithm. We further revealed that the random noise in the diffraction data can dramatically affect similarity quantification. Denoising processes could improve the crystallographic mapping quality. With the three methods mentioned above, we were able to map the crystallographic variants in different materials systems, thus enabling fast variant number quantification and clear variant distribution visualization. The advantages and disadvantages of each approach are also discussed. We expect these methods to benefit researchers who work on martensitic materials, in which the variant information is critical to understand their properties and functionalities.

In this work, we developed a method using precession electron diffraction data to map the residual elastic strain at the nano-scale. The diffraction pattern of each pixel was first collected and denoised. Template matching was then applied using the center spot as the mask to identify the positions of the diffraction disks. Statistics of distances between the selected diffracted disks enable the user to make an informed decision on the reference and to generate strain maps. Strain mapping on an unstrained single crystal sapphire shows the standard deviation of strain measurement is 0.5%. With this method, we were able to successfully measure and map the residual elastic strain in VO₂ on sapphire and martensite in a Ni_50.3Ti_29.7Hf₂₀ shape memory alloy. This approach does not require the user to select a “strain-free area” as a reference and can work on datasets even with the crystals oriented away from zone axes. This method is expected to provide a robust and more accessible alternative means of studying the residual strain of various material systems that complements the existing algorithms for strain mapping.