An Investigation of Physics Informed Neural Networks to solve the Poisson-Boltzmann Equation in Molecular Electrostatics

TL;DR

This paper investigates the use of physics-informed neural networks (PINN) to solve the Poisson-Boltzmann equation in molecular electrostatics, highlighting architecture features that improve accuracy and discussing integration of experimental data.

Contribution

It provides a detailed assessment of PINN architectures for solving the PBE, including novel features like Fourier layers and loss balancing, and offers an open-source implementation.

Findings

PINN accuracy is comparable to previous methods, around 10^{-2} to 10^{-3}.

Incorporating features like Fourier layers and scaling improves model performance.

Challenges remain in applying PINN to nonlinear PBE and integrating experimental data.

Abstract

Physics-informed neural networks (PINN) is a machine learning (ML)-based method to solve partial differential equations that has gained great popularity due to the fast development of ML libraries in the last few years. The Poisson-Boltzmann equation (PBE) is widely used to model mean-field electrostatics in molecular systems, and in this work we present a detailed investigation of the use of PINN to solve the PBE. Starting from a multidomain PINN for the PBE with an interface, we assess the importance of incorporating different features into the neural network architecture. Our findings indicate that the most accurate architecture utilizes input and output scaling layers, a random Fourier features layer, trainable activation functions, and a loss balancing algorithm. The accuracy of our implementation is of the order of 10$^{-2}$ -- $10^{-3}$, which is similar to previous work using PINN to solve other differential equations. We also explore the possibility of incorporating experimental information into the model, and discuss challenges and future work, especially regarding the nonlinear PBE. Along with this manuscript, we are providing an open-source implementation to easily perform computations from a PDB file. We hope this work will motivate application scientists into using PINN to study molecular electrostatics, as ML technology continues to evolve at a high pace.