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Combining Machine Learning Approaches and Accurate Ab Initio Enhanced Sampling Methods for Prebiotic Chemical
Timothée Devergne1, Théo Magrino1, Fabio Pietrucci1
1UMR CNRS 7590, Muséum National d' Histoire Naturelle, Institut de Recherche pour le Développement, Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Sorbonne Université, 75252 Paris, France.
Journal of Chemical Theory and Computation
|August 5, 2022
Summary
This study introduces an efficient ab initio protocol using machine learning (ML) potentials for accurate chemical reaction free-energy profiles. The ML approach significantly reduces computational cost while maintaining high accuracy for complex reactions.
Area of Science:
- Computational Chemistry
- Chemical Reaction Dynamics
- Machine Learning in Science
Background:
- Quantitative prediction of chemical reaction thermodynamics, kinetics, and mechanisms in solution necessitates advanced free-energy methods.
- Atomistic simulation methods, particularly quantum-based ab initio approaches, face significant computational costs for statistically meaningful sampling.
- Extrapolation risks are a common challenge in typical atomistic machine learning (ML) approaches.
Purpose of the Study:
- To critically assess the optimal structure and minimal size of an ab initio training set for accurate free-energy profiles using neural network potentials.
- To propose an ab initio protocol integrating ML to enhance computational efficiency without compromising accuracy.
- To investigate the application of this protocol to computationally challenging reaction steps in the Strecker-cyanohydrin mechanism.
Main Methods:
- Development and assessment of neural network potentials trained on ab initio data.
- Integration of a machine learning-based task within an ab initio protocol.
- Application to two key reaction steps of the Strecker-cyanohydrin mechanism for glycine synthesis in aqueous solution.
Main Results:
- Identified optimal structure and minimal size for ab initio training sets to achieve accurate free-energy profiles with ML potentials.
- Demonstrated that the proposed ML-integrated ab initio protocol achieves indistinguishable accuracy compared to traditional methods.
- Achieved approximately one order of magnitude reduction in computational load for the studied reaction steps.
Conclusions:
- The integration of ML tasks into ab initio calculations offers a significant boost in computational efficiency for free-energy predictions.
- This approach maintains high accuracy and mitigates extrapolation risks inherent in standard atomistic ML methods.
- The developed protocol provides a viable pathway for accurate and efficient computational studies of complex chemical reactions in solution.
