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Fast Generation of Machine Learning-Based Force Fields for Adsorption Energies.

Saientan Bag¹, Manuel Konrad¹, Tobias Schlöder¹

¹Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz Platz 1, Eggenstein-Leopoldshafen 76344, Germany.

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|October 8, 2021

Summary

We developed a novel machine learning approach to create accurate, application-specific force fields for molecular adsorption studies. This method significantly accelerates simulations compared to quantum mechanics, enabling detailed analysis of biomolecule interactions.

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Area of Science:

Computational Chemistry
Materials Science
Biophysics

Background:

Adsorption/desorption are crucial in biomolecule purification, drug delivery, and surface coatings.
Quantum mechanics (QM) simulations are accurate but limited to picoseconds.
Classical molecular dynamics (MD) are faster but limited by force field accuracy.

Purpose of the Study:

To develop a systematic method for generating accurate, flexible, and application-specific force fields using artificial neural networks (ANNs).
To overcome the timescale and accuracy limitations of existing QM and classical simulation methods for adsorption studies.

Main Methods:

Training ANNs to generate novel force fields for molecular adsorption.
Investigating the adsorption of alanine on graphene and gold (111) surfaces as a proof of concept.
Developing a machine learning model incorporating system-specific three-body interactions.

Main Results:

A molecule-specific force field using modified Lennard-Jones potentials (3rd and 7th power terms) showed optimal results.
An efficient ANN model was developed, capturing essential three-body interactions for surfaces like gold.
The final machine learning-based force field achieved <4.2 kJ/mol mean absolute error, offering a ~10^5 speed-up over QM calculations.

Conclusions:

The proposed ANN-based force field generation method provides a significant speed-up and high accuracy for studying molecular adsorption.
This approach enables large-scale simulations of adsorption processes, impacting biomolecule purification, drug carrier engineering, and surface coating design.