5 papers
Symbolic Predicate-Guided Language Agents for Inverse Design of Perovskite Oxides
Dong Hyeon Mok, Seoin Back, Victor Fung +1
Efficient discovery of high-performance materials has been pursued through a variety of data- and AI-driven strategies, among which inverse design, generating materials from desire…
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…
Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework
Dong Hyeon Mok, Seoin Back, Victor Fung +1
Large language models (LLMs) are becoming increasingly applied beyond natural language processing, demonstrating strong capabilities in complex scientific tasks that traditionally…
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…
MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery
Lingyu Kong, Nima Shoghi, Guoxiang Hu +2
Geometric machine learning models such as graph neural networks have achieved remarkable success in recent years in chemical and materials science research for applications such as…