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high-throughput simulations

topic1 events
papersTODAY 04:00 UTC

arXiv paper introduces agentic framework for high-throughput atomistic simulations

A new arXiv preprint describes a system that uses autonomous agents to run machine-learning interatomic potential simulations at scale. The work targets the difficulty of applying these potentials across broad chemical spaces, where near-ab initio accuracy is achieved at much lower computational cost. The abstract frames the effort as addressing a central bottleneck in practical use of such potentials.

agents/machine-learning/simulation/materials-science