Tim Kodalle | Searcharxiv

arXiv2026

Visualizing Crystallization Dynamics and Transformation Pathways of Disordered Rocksalt Oxides During Thermally Activated Sol-Gel Synthesis

Sol-gel synthesis is a wet-chemical processing route for the fabrication of functional materials offering control over composition, morphology, and microstructure at relatively low processing temperatures compared to conventional solid-state synthesis methods. While the sol-gel process initiates with intermixed molecular precursors, the transformation pathways at the early nucleation stage are insufficiently understood. Here, the chemical and structural transformation of disordered rocksalt (DRX) Li1.2Mn0.4Ti0.4O2 (LMTO), a promising cathode material for lithium batteries, is studied by multiscale characterization tools. In situ heating transmission electron microscopy (TEM) using a liquid cell visualizes and identifies crystallization pathways at nanoscale. While some regions follow a classical multi-step transition through thermodynamically stable intermediates, others exhibit a kinetic shortcut in which intermediate nanocrystals dissolve into a localized amorphous matrix that directly precipitates the DRX structure. Macroscale FTIR corroborates the findings to be related to chemically distinct microenvironments in the gel precursor, with transition metal ions more strongly incorporated into the acetate-coordinated network than lithium. Although in situ heating TEM captures diverse local transformation pathways, in situ SXRD indicates that the macroscopic transformation proceeds predominantly through spinel LMTO and lithium titanite intermediates toward DRX-LMTO. The findings shed light on the spatiotemporal chemical and structural transformations in sol-gel derived DRX-LMTO materials, and call for fine tuning of such sol-gel chemistries to manipulate the crystallization pathways and achieve target material homogeneity more efficiently.

cond-mat.mtrl-sci↗

arXiv2025

opXRD: Open Experimental Powder X-ray Diffraction Database

Powder X-ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still presents a significant challenge to automation and a bottleneck in high-throughput discovery in self-driving labs. Machine learning promises to resolve this bottleneck by enabling automated powder diffraction analysis. A notable difficulty in applying machine learning to this domain is the lack of sufficiently sized experimental datasets, which has constrained researchers to train primarily on simulated data. However, models trained on simulated pXRD patterns showed limited generalization to experimental patterns, particularly for low-quality experimental patterns with high noise levels and elevated backgrounds. With the Open Experimental Powder X-Ray Diffraction Database (opXRD), we provide an openly available and easily accessible dataset of labeled and unlabeled experimental powder diffractograms. Labeled opXRD data can be used to evaluate the performance of models on experimental data and unlabeled opXRD data can help improve the performance of models on experimental data, e.g. through transfer learning methods. We collected 92552 diffractograms, 2179 of them labeled, from a wide spectrum of materials classes. We hope this ongoing effort can guide machine learning research toward fully automated analysis of pXRD data and thus enable future self-driving materials labs.

cond-mat.mtrl-sci↗