most citedMOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

cond-mat.mtrl-sci2026

An LLM agent for end-to-end computational materials discovery

Chen Yuntong, Huang Ju, Liu Yu +8

Coordinating multi-scale tasks is an effective strategy for computational materials discovery, yet the diverse algorithms and tools involved make this process highly challenging. H…

cond-mat.mtrl-sci2025★ 2 cited

MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling

Hendrik Kraß, Ju Huang, Seyed Mohamad Moosavi

Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accu…