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Reactive Active Learning: An Efficient Approach for Training Machine Learning Interatomic Potentials for Reacting

Siddarth K Achar¹, Priyanka B Shukla², Chinmay V Mhatre²

¹Computational Modeling & Simulation Program, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States.

Journal of Chemical Theory and Computation

|September 3, 2025

Summary

This summary is machine-generated.

A new reactive active learning (RAL) framework efficiently trains machine learning interatomic potentials (MLIPs) for complex chemical reactions. This approach enables accurate prediction of reaction pathways and discovery of novel catalysts, overcoming limitations of traditional methods.

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Published on: January 25, 2020

Area of Science:

Computational Chemistry
Materials Science
Chemical Engineering

Background:

Quantum mechanics calculations for chemical reactions are computationally expensive and scale poorly.
Machine learning interatomic potentials (MLIPs) offer a faster alternative but struggle with reactive systems due to sampling challenges.
Existing MLIP training methods are not optimized for exploring diverse reaction pathways and transition states.

Purpose of the Study:

To develop a reactive active learning (RAL) framework for efficient training of MLIPs for reactive chemical systems.
To achieve near-quantum mechanical accuracy in MLIPs without prior knowledge of reaction pathways or products.
To enable large-scale simulations for discovering new catalysts and understanding reaction mechanisms.

Main Methods:

Combined automated reaction exploration, uncertainty-driven active learning, and transition state sampling.
Developed a framework to train MLIPs for systems with unknown transition states and products.
Applied the RAL framework to gas-phase ammonia synthesis, solution-phase methanimine hydrolysis, and heterogeneous methane activation on TiC surfaces.

Main Results:

RAL-trained MLIPs accurately predicted reaction barriers and transition states across diverse chemical systems.
Identified Ti2C as a highly active methane activation surface (90% decomposition at 1000 K) via C-vacancy mechanisms.
Enabled simulations of large systems (~900 atoms) over nanosecond timescales, revealing insights into surface poisoning and reaction networks.

Conclusions:

Reactive exploration is crucial for accurately capturing the potential energy surface in MLIPs.
Synergistic chemical and configurational sampling enhances model accuracy.
The RAL framework provides a robust method for computational discovery of catalysts and reaction mechanisms, establishing guidelines for training reactive potentials.