7 citations · 8 across the 4 of their papers we have counts for
4 papers
MatterSim-MT: A multi-task foundation model for in silico materials characterization
Han Yang, Xixian Liu, Chenxi Hu +25
Accurate property characterization is a major bottleneck in materials design. While first-principles methods and task-specific machine-learning models have driven important progres…
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…
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…