Machine Learning Force Fields in Electrochemistry: From Fundamentals to Applications.

Area of Science:

• Computational Materials Science
• Physical Chemistry
• Machine Learning

Background:

• Machine learning force fields (MLFFs) are increasingly vital in materials science.
• Traditional methods struggle with the complexity of atomic interactions in electrochemical systems.

Purpose of the Study:

• To review the fundamental principles and diverse applications of MLFFs in electrochemistry.
• To highlight MLFFs' transformative impact on materials discovery and simulation.

Main Methods:

• Utilizing invariant/equivariant descriptors from body-order expansions for atomic interactions.
• Employing linear regression, kernel methods, or neural networks to build potential energy surfaces.
• Applying MLFFs to molecular dynamics (MD) simulations and thermodynamic integration for electrochemical reactions.

Main Results:

• Accurate prediction of ionic conductivity and redox potentials in various electrolyte systems.
• Enabling high-throughput screening of millions of crystal structures for materials discovery.
• Facilitating extraction of thermodynamic and kinetic data for coarse-grained modeling.

Conclusions:

• MLFFs are a core technology in computational materials science, bridging high-precision calculations and large-scale exploration.
• MLFFs are crucial for advancing the design and discovery of novel electrochemical materials.
• Future directions involve continued evolution of MLFFs for broader applications in energy and beyond.