MALOQ: Massively Accelerated Learning of Operators for Quantum Transport

• URL: http://arxiv.org/abs/2606.28911v1
• Date: Sat, 27 Jun 2026 13:34:52 GMT
• Title: MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
• Abstract summary: MALOQ is an application built to train on and predict electronic-structure matrices for systems made of few to 100k atoms.<n>It reduces time-per-epoch by over 30% compared to a molecule-wise-distributed framework.<n>We demonstrate scalable training and inference for 3,000-12,000 atoms on the Alps supercomputer.
• Score: 71.9141151643915
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extending electronic-structure calculations to previously unfeasible scales. Here, we introduce MALOQ (Massively Accelerated Learning of Operators for Quantum Transport), an application built to train on and predict electronic-structure matrices for systems made of few to 100k atoms, described by large basis sets, and covering a wide range of atomic elements. Based on a state-of-the-art, SO(2)-equivariant backbone architecture, MALOQ provides (i) custom data-processing kernels to handle high-rank Hamiltonian matrix data and (ii) a scalable edge-wise distribution of atomic graph(s). Trained on the largest molecular Hamiltonian datasets available today, it reduces time-per-epoch by over 30% compared to a molecule-wise-distributed framework, and enables inference on material graphs of arbitrary size. We demonstrate scalable training and inference for 3,000-12,000 atoms on the Alps supercomputer, up to 192 GPUs and 256 GPUs, respectively.

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