Machine Learning Approaches for Developing Potential Surfaces: Applications to OH-(H2O) (n = 1-3) Complexes | JoVE Visualize

Area of Science:

Computational Chemistry
Quantum Mechanics
Machine Learning in Chemistry

Background:

Accurate potential energy surfaces are crucial for understanding molecular behavior.
Traditional methods for calculating these surfaces are computationally expensive.
Machine learning offers a promising avenue for accelerating these calculations.

Purpose of the Study:

To develop a novel, two-step machine learning approach for high-level *ab initio* potential surface generation.
To expand the molecular-orbital based machine learning (MOB-ML) model for learning correlation energies at the complete basis set limit.
To create accurate neural network potentials for systems like hydroxide and hydronium ions interacting with water molecules.

Main Methods:

Utilized Gaussian process regression within the MOB-ML model to learn correlation energies.
Employed smaller basis set orbitals (aug-cc-pVDZ) as features for predicting complete basis set limit energies.
Integrated MOB-ML with neural network potentials, using diffusion Monte Carlo (DMC) sampled geometries and energies for training.
Developed protocols to optimize the use of DMC-generated structures in the training process.

Main Results:

Successfully developed and applied MOB-ML combined with neural networks to generate potential surfaces for OH⁻(H₂O) and H₃O⁺(H₂O).
DMC calculations using the new potentials showed good agreement with previous results for these floppy molecular systems.
Generated novel potential surfaces for larger systems: OH⁻(H₂O)₂ , OH⁻(H₂O)₃, H₃O⁺(H₂O)₂, and H₃O⁺(H₂O)₃.
Found similar proton delocalization levels between hydroxide and hydronium ions bound to the same number of water molecules.

Conclusions:

The combined MOB-ML and neural network approach provides an efficient route to high-accuracy potential surfaces.
The developed potentials enable reliable simulations of hydrated proton and hydroxide systems.
The study highlights similarities in proton delocalization for hydroxide and hydronium systems, consistent with experimental spectral observations.