核心概念
Machine learning potentials can accurately predict free energy surfaces but require comprehensive training datasets.
摘要
This article explores the accuracy of machine learning potentials (MLPs) in predicting free energy surfaces (FES) using Metadynamics simulations. It investigates the impact of collective variable distributions on MLP accuracy, focusing on butane and alanine dipeptide molecules. The study reveals that MLPs trained with diverse configurations show better prediction accuracy, emphasizing the importance of comprehensive training datasets for accurate FES predictions.
Directory:
- Abstract: MLPs aim to describe FES accurately and efficiently.
- Introduction: CVs reduce system dimensionality to study metastable states.
- Methods: Training data construction for butane and ADP using CLMD and SPC.
- Allegro Model: Hyperparameters optimization for MLP training.
- DPMD Simulations: Unbiased simulations' stability and limitations in ADP models.
- Results & Discussions: MLP accuracy in predicting FES for butane and ADP.
- Deep Potential Metadynamics Simulations: Predicted FES results analysis.
統計資料
"The MAE was 0.008 kcal/mol, corresponding to 0.571 × 10−3 kcal/(mol atom)."
"The MAE ranged from 3.393 to 1.644 kcal/mol for ADP MLPs."
"The model's accuracy is supported by the MAE of 0.069 kcal/mol."
"The percentage error for potential energy predictions under 0.25%."