# Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields Source: [Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields]() Collection index: [llms.txt]() Data updated: 2025-10-27T03:53:38.362Z Authors: Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert M\\"uller, Alexandre Tkatchenko Published: 2018-02-26T10:32:56.000Z ## Abstract Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Force Prediction on MD17 Ethanol](): Force Prediction; MD17 Ethanol; MAE (kcal/mol/Å): 0.33. - [Atomic force prediction on MD17 (test)](): Atomic force prediction; MD17 (test); Force Error (Benzene): 0.2. - [Energy Prediction on MD17 Salicylic acid](): Energy Prediction; MD17 Salicylic acid; MAE (kcal/mol): 0.12. - [Energy Prediction on MD17 Toluene](): Energy Prediction; MD17 Toluene; MAE (kcal/mol): 0.1. - [Force Prediction on MD17 Naphthalene](): Force Prediction; MD17 Naphthalene; MAE (kcal/mol/Å): 0.11. - [Force Prediction on MD17 Toluene](): Force Prediction; MD17 Toluene; MAE (kcal/mol/Å): 0.14. - [Energy Prediction on MD17 Aspirin](): Energy Prediction; MD17 Aspirin; MAE (kcal/mol): 0.19. - [Energy Prediction on MD17 Uracil](): Energy Prediction; MD17 Uracil; MAE (kcal/mol): 0.11. - [Force Prediction on MD17 Uracil](): Force Prediction; MD17 Uracil; MAE (kcal/mol/Å): 0.24. - [Force Prediction on MD17 Aspirin](): Force Prediction; MD17 Aspirin; MAE (kcal/mol/Å): 0.68. - [Energy Prediction on MD17 Naphthalene](): Energy Prediction; MD17 Naphthalene; MAE (kcal/mol): 0.12. - [Energy Prediction on MD17 Malonaldehyde](): Energy Prediction; MD17 Malonaldehyde; MAE (kcal/mol): 0.1. - [Force Prediction on MD17 Salicylic acid](): Force Prediction; MD17 Salicylic acid; MAE (kcal/mol/Å): 0.28. - [Energy Prediction on MD17 Ethanol](): Energy Prediction; MD17 Ethanol; MAE (kcal/mol): 0.07. - [Force Prediction on MD17 Malonaldehyde](): Force Prediction; MD17 Malonaldehyde; MAE (kcal/mol/Å): 0.41. ## Original sources and implementation [Original paper]() [PDF]()