Tingzheng Hou | Search arXiv

arXiv2026

pymatgen: 15 years of community growth, new functionality, and future prospects

We present a retrospective on the Python Materials Genomics (pymatgen) library, reflecting on 15 years of community growth and established best practices. The current version of pymatgen builds on its robust, open-source foundation, emphasising its collaborative nature. Over the past decade, pymatgen has thrived as one of the largest open-source materials science codebases. We detail how pymatgen supports advanced computational materials science workflows, how it adapts to changing demands, and the lessons learned from its growing community, providing guidance beyond pymatgen's open-source community. Pymatgen will continue to evolve in response to the current rapidly changing landscape of computational materials science and materials informatics. We discuss the challenges and potential solutions to managing the community-driven evolution of pymatgen and provide an outlook on its future development.

cond-mat.mtrl-sci↗

arXiv2025

4D-MISR: A unified model for low-dose super-resolution imaging via feature fusion

While electron microscopy offers crucial atomic-resolution insights into structure-property relationships, radiation damage severely limits its use on beam-sensitive materials like proteins and 2D materials. To overcome this challenge, we push beyond the electron dose limits of conventional electron microscopy by adapting principles from multi-image super-resolution (MISR) that have been widely used in remote sensing. Our method fuses multiple low-resolution, sub-pixel-shifted views and enhances the reconstruction with a convolutional neural network (CNN) that integrates features from synthetic, multi-angle observations. We developed a dual-path, attention-guided network for 4D-STEM that achieves atomic-scale super-resolution from ultra-low-dose data. This provides robust atomic-scale visualization across amorphous, semi-crystalline, and crystalline beam-sensitive specimens. Systematic evaluations on representative materials demonstrate comparable spatial resolution to conventional ptychography under ultra-low-dose conditions. Our work expands the capabilities of 4D-STEM, offering a new and generalizable method for the structural analysis of radiation-vulnerable materials.

cs.CV↗