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Architected Liquid Crystal Elastomer Lattices with Programmable Energy Absorption
Soft, energy absorbing materials are widely used in protective gear, biomedical devices, and robotics. Lewis and her collaborators at Lawrence Livermore National Laboratory (LLNL), demonstrated that printed and aligned liquid crystal elastomer (LCE) lattices exhibit superior energy absorption compared to silicone elastomers.
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Three-Dimensional Photochemical Printing of Thermally Activated Polymer Foams
This work demonstrates the facile, on-demand manufacturing of polymer foams with desirable properties such as mechanical strength, controlled porosity, and varied composition.
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Synthesis of Borophane Polymorphs through Hydrogenation of Borophene
In a three PI collaboration within NU-MRSEC IRG-1, “borophane” polymorphs have been synthesized by hydrogenating borophene with atomic hydrogen in ultrahigh vacuum. Borophane polymorphs are metallic and can be reversibly returned to pristine borophene through thermal desorption of hydrogen.
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Inverse Design of Mechanical Metamaterials with Target Nonlinear Response via a Neural Accelerated Evolution Strategy
A team at the Harvard MRSEC led by Bertoldi and Rycroft has developed a framework to design mechanical metamaterials with target nonlinear response. Neural networks were used to accurately learn the relationship between the geometry and nonlinear mechanical response of these metamaterials.
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Partnership in Research and Education in Materials with Navajo Tech: Inspiring STEM Pathways from High School to Graduate Studies
The Partnership for Research and Education in Materials between Navajo Technical University and the MRSEC based at Harvard focuses on developing culturally-informed, sustainable pathways into materials science-related careers and advanced studies for Navajo students.
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Interpretable ML for Crystal Energy Landscapes Using Kolmogorov-Arnold Networks
The University of Tennessee, Knoxville's Center for Advanced Materials and Manufacturing has introduced the Element-Weighted Kolmogorov–Arnold Network, a novel interpretable ML architecture that predicts crystal energy landscape properties — formation energy, band gap, and work function — directly from chemical composition. EWKAN achieves state-of-the-art accuracy across large-scale databases, matching or exceeding GNN-based models that require full 3D atomic structure inputs, while using orders of magnitude fewer parameters.
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Speaker Kits to Engage Middle Schoolers in Magnetism Science
In February 2024, I-MRSEC investigator Daniel Shoemaker, grad student Emily Waite, and outreach coordinator Pamela Pena Martin taught 35 7th and 8th graders at Franklin STEAM Academy, a Champaign public middle school, about magnetism through a kit they developed, supported by the I-MRSEC and a grant from the APS Group on Magnetism and its Applications. This visit was part of an annual 7-week program that teaches materials science concepts through hands-on activities aimed to build interest and confidence in STEM.
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Frustrated self-limiting assembly of trumpets
Triangular monomers with positive curvature in one direction and negative curvature in another assemble into trumpet shaped objects predicted to have precise self-limited lengths due to frustration-induced stress.
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Accelerated Discovery of Thermoelectric Heteroanionic Materials
Northwestern University MRSEC IRG-2 has developed an efficient theoretical framework based on high-throughput density functional theory calculations and machine learning methods to accelerate the discovery of heteroanionic materials.
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AI Enabled Quantum Chemical Accuracy for Helium-Benzene Interactions
An interdisciplinary team of CAMM IRG1 researchers developed a quantitatively reliable helium-benzene potential energy surface with quantum chemical accuracy by combining CCSD(T)/CBS electronic-structure and a multifidelity Gaussian process model that merges sparse high-accuracy data with dense lower-cost DFT data. This is an important result for a weakly bound quantum system in which small errors in the interaction potential lead to materially different many-body predictions.
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