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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training

Adam Lahouari¹, Jutta Rogal^1,2, Mark E Tuckerman^1,3,4,5,6

¹Department of Chemistry, NYU, New York, New York 10003, United States.

Journal of Chemical Theory and Computation

|December 26, 2025

Summary

This summary is machine-generated.

We developed an Automated Machine Learning Pipeline (AMLP) to streamline the creation and validation of machine learning interatomic potentials (MLIPs). This pipeline achieves near-quantum accuracy for molecular simulations at a lower computational cost.

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Area of Science:

Computational Chemistry
Materials Science
Artificial Intelligence

Background:

Machine learning interatomic potentials (MLIPs) offer near-quantum accuracy for molecular simulations but are challenging to develop.
Current MLIP development requires extensive data generation, structure preprocessing, and model training/validation.

Purpose of the Study:

To introduce an Automated Machine Learning Pipeline (AMLP) that integrates the entire MLIP workflow.
To leverage large-language-model agents for automating code selection, input preparation, and output conversion.

Main Methods:

The AMLP pipeline utilizes large-language-model agents and the MACE architecture.
An analysis suite (AMLP-Analysis) based on ASE supports various molecular simulations.
The pipeline was validated on acridine polymorphs.

Main Results:

Fine-tuning a foundation model achieved mean absolute errors of 1.7 meV/atom (energies) and 7.0 meV/Å (forces).
The resulting MLIP accurately reproduced DFT geometries (sub-Å accuracy).
The MLIP demonstrated stability in microcanonical and canonical ensemble molecular dynamics simulations.

Conclusions:

The AMLP significantly simplifies and automates the development of reliable MLIPs.
The developed MLIPs provide accurate and stable simulations, extending the capabilities of computational methods.