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Congratulations to Kenna Ashen and Xiaofeng Qian for their publication “Domain-dependent strain and stacking in two-dimensional van der Waals ferroelectrics” in Nature Communications. View at https://doi.org/10.1038/s41467-023-42947-3

Congratulations to Manuel Quiroz, Dakota Jones, Kim Dunbar, and Marcetta Darensbourg for their publication “Magnetic Coupling between Fe(NO) Spin Probe Ligands Through Diamagnetic NiII, PdII and PtII Tetrathiolate Bridges” in Chemical Science. View at https://doi.org/10.1039/D3SC01546G

Congratulations to George Agbeworvi, Saul Perez-Beltran, Perla Balbuena, and Sarbajit Banerjee for their publication “Toggling Stereochemical Activity through Interstitial Positioning of Cations between 2D V2O5 Double Layers” in ACS Chemistry of Materials. View at https://doi.org/10.1021/acs.chemmater.3c01463

Congratulations to Adelaide Bradicich, Timothy Brown, Stanley Williams, and Patrick Shamberger for their publication “Spontaneous Symmetry-Breaking of Nonequilibrium Steady-States Caused by Nonlinear Electrical Transport” in Advanced Electronic Materials. View at https://doi.org/10.1002/aelm.202300265

Congratulations to Sarbajit Banerjee, Patrick Shamberger, and Matt Pharr on their publication “Crystallographic variant mapping using precession electron diffraction data” in Microstructures. View at https://doi.org/10.20517/microstructures.2023.17

Congratulations to Patrick Shamberger, Sarbajit Banerjee, and Matt Pharr on their publication “A Reference-area free Strain Mapping Method Using Precession Electron Diffraction Data” in Ultramicroscopy. View at https://doi.org/10.1016/j.ultramic.2023.113700

Congratulations to George Agbeworvi and Sarbajit Banerjee for their publication “Protecting groups in insertion chemistry: Site-selective positioning of lithium ions in intercalation hosts” in Matter. View at https://doi.org/10.1016/j.matt.2023.01.028

reMIND EFRC awarded in 2022. The vision of reMIND is to establish foundational scientific knowledge underpinning the function of massively reconfigurable computing architectures that approach fundamental limits of energy efficiency and speed, enabling real-time learning and embedded intelligence. The EFRC is a collaboration among the College of Engineering, the Department of Chemistry, the Texas A&M Engineering Experiment Station, the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory and Sandia National Laboratories.