Summary
This benchmark evaluates the quality and accuracy of Machine Learning Interatomic Potentials (MLIP) by analyzing the conformational sampling of amino acids in small proteins during molecular dynamics simulations. Specifically, it computes backbone Ramachandran angles (phi/psi) and side chain rotamer angles (chi1, chi2, …). The sampled probability distribution of these angles is then compared against reference data and outliers are detected.
Interactive features
No response
Category
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Data availability
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Computational cost
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Additional context
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Summary
This benchmark evaluates the quality and accuracy of Machine Learning Interatomic Potentials (MLIP) by analyzing the conformational sampling of amino acids in small proteins during molecular dynamics simulations. Specifically, it computes backbone Ramachandran angles (phi/psi) and side chain rotamer angles (chi1, chi2, …). The sampled probability distribution of these angles is then compared against reference data and outliers are detected.
Interactive features
No response
Category
No response
Data availability
No response
Computational cost
No response
Additional context
No response