9 papers
Atomistic Machine Learning with Irreducible Cartesian Natural Tensors
Qun Chen, A. S. L. Subrahmanyam Pattamatta, Boyu Wang +2
Atomistic machine learning is a powerful tool for accurate and efficient investigation of material behavior at the atomic scale. While attempts have been made to construct models d…
MatTools: Benchmarking Large Language Models for Materials Science Tools
Siyu Liu, Bo Hu, Beilin Ye +3
Large language models (LLMs) are increasingly applied to materials science questions, including literature comprehension, property prediction, materials discovery and alloy design.…
Electron hopping induced phonon pumping in opto-mechanical molecular nanocavities
Yu Bai, Ilya Razdolski, Zhizi Guan +5
Plasmonic molecular nanojunctions exhibit opto-mechanical coupling at the nanoscale, enabling intertwined optical, vibrational and electronic phenomena. Here, we demonstrate plasmo…
Why Grain Growth is Not Curvature Flow
Caihao Qiu, David J. Srolovitz, Gregory S. Rohrer +2
Grain growth in polycrystals is traditionally considered a capillarity-driven process, where grain boundaries (GBs) migrate toward their centers of curvature (i.e., mean curvature…
A Mesoscale Model for Interface-Mediated Plasticity: Investigation of Ductile and Brittle Fracture
Jinxin Yu, Alfonso H. W. Ngan, David J. Srolovitzb +1
The presence of interfaces and grain boundaries significantly impacts the mechanical properties of materials, particularly when dealing with micro- or nano-scale samples. Distinct…
Hot-electron-injection-induced symmetry breaking in bilayer MoS probed by second-harmonic generation
Zhizi Guan, Zhiwei Peng, David J. Srolovitz +2
Symmetry governs the selection rules of light-matter interactions in crystalline materials, making symmetry manipulation a powerful tool for tuning their optical properties. Here,…