Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs

2026-05-05

AI Summary

This work addresses the challenge of inefficient training of machine-learned interatomic potentials (MLIPs) in reaction chemistry, where high-cost quantum chemical labels and scarcity of transition-state configurations severely limit data availability. The authors propose a novel active learning strategy that eschews auxiliary uncertainty modules, Bayesian training, or ensemble methods, instead leveraging the latent representations of a pretrained MACE-based MLIP to construct an acquisition function guided by the finite-width neural tangent kernel (NTK) and activation kernel. They demonstrate for the first time that the latent space of a pretrained MLIP inherently provides efficient and reliable acquisition signals, whose geometric structure aligns with model error while preserving chemical interpretability. On reaction chemistry benchmarks, the method reduces by 38% and 28%, respectively, the amount of data required to achieve baseline energy and force errors, substantially improving data efficiency and uncertainty estimation.

Abstract

Training machine learning interatomic potentials (MLIPs) for reactive chemistry is often bottlenecked by the high cost of quantum chemical labels and the scarcity of transition state configurations in candidate pools. Active learning (AL) can mitigate these costs, but its effectiveness hinges on the acquisition rule. We investigate whether the latent space of a pretrained MLIP already contains the information necessary for effective acquisition, eliminating the need for auxiliary uncertainty heads, Bayesian training and fine-tuning, or committee ensembles. We introduce two acquisition signals derived directly from a pretrained MACE potential: a finite-width neural tangent kernel (NTK) and an activation kernel built from hidden latent space features. On reactive-chemistry benchmarks, both kernels consistently outperform fixed-descriptor baselines, committee disagreement, and random acquisition, reducing the data required to reach performance targets by an average of 38% for energy error and 28% for force error. We further show that the pretrained model induces similarity spaces that preserve chemically meaningful structure and provide more reliable residual uncertainty estimates than randomly initialised or fixed-descriptor-based kernels. Our results suggest that pretraining aligns latent-space geometry with model error, yielding a practical and sufficient acquisition signal for reactive MLIP fine-tuning.

Problem

• machine learning interatomic potentials
• active learning
• reactive chemistry
• quantum chemical labels
• transition state configurations

Innovation

• pretrained representations
• active learning
• neural tangent kernel
• machine learning interatomic potentials
• latent space acquisition