5 papers
Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials
Seungjun Cha, Chen Wang, Victor Fung +1
Halide perovskite nanocrystals are leading candidates for next-generation optoelectronics, yet the role of surface ligands in controlling their phonon dynamics remains poorly under…
Determining Atomic Structure from Spectroscopy via an Active Learning Framework
Ian Slagle, Faisal Alamgir, Victor Fung
Determining atomic structure from spectroscopic data is central to materials science but remains restricted to a limited set of techniques and material classes, largely due to the…
Improving Reliability of Machine Learned Interatomic Potentials With Physics-Informed Pretraining
Qianyu Zheng, Victor Fung
Machine learned interatomic potentials (MLIPs) have emerged as powerful tools for molecular dynamics (MD) simulations with their competitive accuracy and computational efficiency.…
Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning
Lingyu Kong, Jaeheon Shim, Guoxiang Hu +1
Atomistic foundation models (AFMs) have great promise as accurate interatomic potentials, and have enabled data-efficient molecular dynamics simulations with near quantum mechanica…
A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al-Fahdi +3
The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. Whil…