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cond-mat.mtrl-sci2026
Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
Connor W. Edwards, Jack D. Evans
Universal machine-learned interatomic potentials (uMLIPs) offer a promising approach to performing atomistic simulations at near-DFT accuracy with greatly reduced computational cos…
cond-mat.mtrl-sci2026★ 2 cited
QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities
Fengxu Yang, Jack D. Evans
The integration of large language models (LLMs) into materials science offers a transformative opportunity to streamline computational workflows, yet current agentic systems remain…