206 citations · 814 across the 42 of their papers we have counts for
60 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…
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…
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.…
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,…
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models
Zhuoyuan Li, Siyu Liu, Beilin Ye +2
Artificial intelligence (AI) is transforming materials science, enabling both theoretical advancements and accelerated materials discovery. Recent progress in crystal generation mo…
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…