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Articles

Benchmarking universal machine learning interatomic potentials for supported nanoparticles: decoupling energy accuracy from structural exploration

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By Jiayan Xu, Abhirup Patra, Amar Pathak, Sharan Shetty Verified, Roberto Car, Detlef Hohl
| Nature Verified
Abstract Supported nanoparticle catalysts are widely used in the chemical industry. Computational modeling of supported nanoparticles based on density functional theory (DFT) often involves structural searches of stable local minimum energy configurations and molecular dynamics simulations at finite temperature. These are computationally demanding tasks that are intractable within DFT for large systems.
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