AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials | JoVE Visualize

Area of Science:

Computational chemistry
Materials science
Chemical physics

Background:

Accurate computation of intermolecular interactions is crucial but computationally expensive with ab initio methods.
Existing machine learning (ML) potentials often lack physically realistic potential energy surfaces and exhibit deficiencies due to atomic energy partitioning.
There is a need for ML models that provide accurate interaction energies and physically meaningful potential energy surfaces for intermolecular systems.

Purpose of the Study:

To develop a novel machine learning framework and model (AP-Net) for accurate and physically interpretable computation of intermolecular interaction energies.
To address the limitations of existing ML potentials, particularly concerning the smoothness and asymptotic behavior of potential energy surfaces.
To demonstrate the transferability and accuracy of AP-Net across diverse chemical systems.

Main Methods:

Introduction of a physically motivated atomic-pairwise framework for intermolecular ML potentials.
Development of AP-Net, a neural network model leveraging the atomic-pairwise paradigm and symmetry adapted perturbation theory (SAPT) interpretability.
Training and validation of AP-Net on datasets including hydrogen-bonded dimers and the S66x8 dataset.

Main Results:

AP-Net generates smooth, physically meaningful intermolecular potentials with correct asymptotic behavior, unlike previous models.
The model shows significant transferability, accurately predicting energies for the diverse S66x8 dataset after training on limited data.
AP-Net achieved a mean absolute error of 0.37 kcal mol⁻¹ on experimental hydrogen-bonded dimers, with 2-5 fold error reduction in SAPT components.

Conclusions:

The proposed atomic-pairwise framework and AP-Net model represent a significant advancement in ML potentials for intermolecular interactions.
AP-Net successfully learns the underlying physics of intermolecular forces, including electrostatic interactions in hydrogen bonds.
The model's accuracy, interpretability, and transferability offer a promising alternative to traditional computational methods for studying molecular interactions.