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Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine.
Fuchun Ge1, Ran Wang1, Chen Qu2
1State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China.
Machine learning potentials (MLPs) show simulation inaccuracies despite low test errors. A new simulation-oriented approach improves accuracy for chemical simulations, validated by diffusion Monte Carlo.
Area of Science:
- Computational Chemistry
- Materials Science
- Chemical Physics
Background:
- Machine learning potentials (MLPs) are crucial for efficient potential energy surface (PES) representation in chemical simulations.
- Standard evaluation of MLPs uses root-mean-square errors on test sets from the same data distribution.
- The reliability of these test errors for predicting simulation accuracy is often assumed but not rigorously verified.
Purpose of the Study:
- To investigate the correlation between standard test errors of MLPs and their actual performance in various chemical simulation tasks.
- To develop and validate a simulation-oriented strategy for enhancing MLP accuracy.
Main Methods:
- Systematic investigation of MLP performance across different simulation metrics (conformer energies, barriers, vibrational levels, ZPVE).
- Development of an accessible, simulation-focused method to improve MLP quality.
- Validation of the improved MLPs using diffusion Monte Carlo simulations.
Main Results:
- Test set errors do not consistently predict MLP accuracy for diverse simulation properties.
- The proposed simulation-oriented approach significantly enhances MLP precision, particularly for relative conformer energies and reaction barriers.
- The improved MLPs demonstrated high accuracy in rigorous diffusion Monte Carlo simulations.
Conclusions:
- Standard test error metrics for MLPs can be misleading regarding simulation accuracy.
- A simulation-oriented approach is essential for developing high-fidelity MLPs for chemical dynamics.
- The developed method offers a practical solution for improving MLP reliability in computational chemistry.
