Active Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material Systems

• URL: http://arxiv.org/abs/2601.06916v1
• Date: Sun, 11 Jan 2026 13:52:28 GMT
• Title: Active Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material Systems
• Authors: Mohammed Azeez Khan, Aaron D'Souza, Vijay Choyal,
• Abstract summary: We develop an active learning framework that iteratively selects informative training structures for machine-learned interatomic potentials.<n>We show that intelligent data selection strategies can achieve target accuracy with 5-13% fewer labeled samples compared to random baselines.<n>The entire pipeline executes on Google Colab in under 4 hours per system using less than 8 GB of RAM.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient discovery of new materials demands strategies to reduce the number of costly first-principles calculations required to train predictive machine learning models. We develop and validate an active learning framework that iteratively selects informative training structures for machine-learned interatomic potentials (MLIPs) from large, heterogeneous materials databases, specifically the Materials Project and OQMD. Our framework integrates compositional and property-based descriptors with a neural network ensemble model, enabling real-time uncertainty quantification via Query-by-Committee. We systematically compare four selection strategies: random sampling (baseline), uncertainty-based sampling, diversity-based sampling (k-means clustering with farthest-point refinement), and a hybrid approach balancing both objectives. Experiments across four representative material systems (elemental carbon, silicon, iron, and a titanium-oxide compound) with 5 random seeds per configuration demonstrate that diversity sampling consistently achieves competitive or superior performance, with particularly strong advantages on complex systems like titanium-oxide (10.9% improvement, p=0.008). Our results show that intelligent data selection strategies can achieve target accuracy with 5-13% fewer labeled samples compared to random baselines. The entire pipeline executes on Google Colab in under 4 hours per system using less than 8 GB of RAM, thereby democratizing MLIP development for researchers globally with limited computational resources. Our open-source code and detailed experimental configurations are available on GitHub. This multi-system evaluation establishes practical guidelines for data-efficient MLIP training and highlights promising future directions including integration with symmetry-aware neural network architectures.

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