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

Adsorption and desorption
of molecules are key processes in extraction
and purification of biomolecules, engineering of drug carriers, and
designing of surface-specific coatings. To understand the adsorption
process on the atomic scale, state-of-the-art quantum mechanical and
classical simulation methodologies are widely used. However, studying
adsorption using a full quantum mechanical treatment is limited to
picoseconds simulation timescales, while classical molecular dynamics
simulations are limited by the accuracy of the existing force fields.
To overcome these challenges, we propose a systematic way to generate
flexible, application-specific highly accurate force fields by training
artificial neural networks. As a proof of concept, we study the adsorption
of the amino acid alanine on graphene and gold (111) surfaces and
demonstrate the force field generation methodology in detail. We find
that a molecule-specific force field with Lennard-Jones type two-body
terms incorporating the 3rd and 7th power of the inverse distances
between the atoms of the adsorbent and the surfaces yields optimal
results, which is surprisingly different from typical Lennard-Jones
potentials used in traditional force fields. Furthermore, we present
an efficient and easy-to-train machine learning model that incorporates
system-specific three-body (or higher order) interactions that are
required, for example, for gold surfaces. Our final machine learning-based
force field yields a mean absolute error of less than 4.2 kJ/mol at
a speed-up of ∼105 times compared to quantum mechanical
calculation, which will have a significant impact on the study of
adsorption in different research areas.