Accelerating High-Throughput Catalyst Screening by Direct Generation of Equilibrium Adsorption Structures

• URL: http://arxiv.org/abs/2512.15228v1
• Date: Wed, 17 Dec 2025 09:26:58 GMT
• Title: Accelerating High-Throughput Catalyst Screening by Direct Generation of Equilibrium Adsorption Structures
• Authors: Songze Huo, Xiao-Ming Cao,
• Abstract summary: We present DBCata, a deep generative model that integrates a periodic Brownian-bridge framework with an equivariant graph neural network to establish a low-dimensional transition manifold between unrelaxed and DFT-relaxed structures.<n>Upon training, DBCata effectively generates high-fidelity, interatomic distance mean absolute error (DMAE) of 0.035 text on the Catalysis-Hub dataset.<n>The corresponding DFT accuracy can be improved within 0.1 eV in 94% of instances by identifying and refining anomalous predictions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The adsorption energy serves as a crucial descriptor for the large-scale screening of catalysts. Nevertheless, the limited distribution of training data for the extensively utilised machine learning interatomic potential (MLIP), predominantly sourced from near-equilibrium structures, results in unreliable adsorption structures and consequent adsorption energy predictions. In this context, we present DBCata, a deep generative model that integrates a periodic Brownian-bridge framework with an equivariant graph neural network to establish a low-dimensional transition manifold between unrelaxed and DFT-relaxed structures, without requiring explicit energy or force information. Upon training, DBCata effectively generates high-fidelity adsorption geometries, achieving an interatomic distance mean absolute error (DMAE) of 0.035 \textÅ on the Catalysis-Hub dataset, which is nearly three times superior to that of the current state-of-the-art machine learning potential models. Moreover, the corresponding DFT accuracy can be improved within 0.1 eV in 94\% of instances by identifying and refining anomalous predictions through a hybrid chemical-heuristic and self-supervised outlier detection approach. We demonstrate that the remarkable performance of DBCata facilitates accelerated high-throughput computational screening for efficient alloy catalysts in the oxygen reduction reaction, highlighting the potential of DBCata as a powerful tool for catalyst design and optimisation.

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