Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds

• URL: http://arxiv.org/abs/2602.16908v1
• Date: Wed, 18 Feb 2026 21:48:35 GMT
• Title: Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials on organic and inorganic compounds
• Authors: G. Laskaris, D. Morozov, D. Tarpanov, A. Seth, J. Procelewska, G. Sai Gautam, A. Sagingalieva, R. Brasher, A. Melnikov,
• Abstract summary: Allegro is a machine learning interatomic potential (MLIP) model designed to predict atomic properties in molecules.<n>There tends to be a trade-off between accuracy and inference time when training this model.<n>We make variants of Allegro some by adding strictly classical multi-layer perceptron layers.<n>We compare the results from QM9, rMD17-aspirin, rMD17-benzene and our own proprietary dataset consisting of copper and lithium atoms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Allegro is a machine learning interatomic potential (MLIP) model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. For this reason we apply multi-objective hyperparameter optimization to the two objectives. Additionally, we experiment with modified architectures by making variants of Allegro some by adding strictly classical multi-layer perceptron (MLP) layers and some by adding quantum-classical hybrid layers. We compare the results from QM9, rMD17-aspirin, rMD17-benzene and our own proprietary dataset consisting of copper and lithium atoms. As results, we have a list of variants that surpass the Allegro in accuracy and also results which demonstrate the trade-off with inference times.

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