3 papers
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
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…
cs.CL2025
Large language models in materials science and the need for open-source approaches
Fengxu Yang, Weitong Chen, Jack D. Evans
Large language models (LLMs) are rapidly transforming materials science. This review examines recent LLM applications across the materials discovery pipeline, focusing on three key…