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Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

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How Long Is Long Enough? Extrapolation of Machine-Learning Interatomic Potentials for Oligomeric and Polymeric

Natalie E Hooven¹, Arthur Y Lin¹, Charles H Carroll¹

¹Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.

Journal of Chemical Theory and Computation

|June 19, 2026

Summary

This summary is machine-generated.

Machine-learning interatomic potentials (MLIPs) can accurately simulate larger molecules if trained on smaller systems where local chemical environments match. Careful neighbor list construction is key for predicting polymer behavior using these transferable MLIPs.

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Area of Science:

Computational Chemistry
Materials Science
Machine Learning

Background:

Machine-learning interatomic potentials (MLIPs) offer high-fidelity molecular simulations at larger scales.
Training MLIPs for large molecules (polymers, biomolecules) is challenging due to the difficulty of obtaining ab initio data.
Smaller, analogous chemical systems are often used to train MLIPs for complex molecular architectures.

Purpose of the Study:

To investigate the conditions under which MLIPs trained on small molecules can accurately extrapolate to longer molecular chains and complex architectures.
To establish criteria for developing transferable MLIPs for macromolecular materials.
To provide a data-driven approach for designing effective MLIPs.

Main Methods:

A control study using linear alkanes (n=1-8) to assess MLIP extrapolation capabilities.
Analysis of MLIP performance combined with environment-resolved Smooth Overlap of Atomic Positions (SOAP) descriptors.
Application of Principal Covariates Classification (PCC) to identify key chemical environments.
Investigation of neighbor list construction's impact on intermolecular energetics.

Main Results:

Reliable extrapolation of MLIPs to longer chains is achieved when local chemical environments in training and target systems converge.
The accuracy of MLIPs for polymeric behavior is critically dependent on the accurate learning of intermolecular energetics.
Optimized neighbor list construction significantly enhances the learnability of intermolecular interactions.

Conclusions:

Transferable MLIPs for macromolecular materials can be designed by ensuring convergence of local chemical environments between training and target systems.
Neighbor list optimization is crucial for accurately capturing intermolecular energetics, essential for simulating polymer properties.
This study provides a practical framework for developing robust MLIPs for large-scale molecular simulations.