Democratizing Atomistic Simulation Workflows for the AI Era with the Quantum Accelerator
Abstract
We present the Quantum Accelerator (QuAcc), an open-source workflow library for atomistic simulations with an emphasis on quantum-mechanical calculations. QuAcc provides predefined workflow recipes spanning first-principles electronic-structure methods, semiempirical and tight-binding approaches, classical potentials, and foundation machine-learned interatomic potentials (MLIPs). A central design feature of QuAcc is its separation of domain-specific scientific logic from the workflow engine used to orchestrate and execute calculations. Workflows are written as ordinary Python functions and can be executed with multiple supported workflow engines without modifying the underlying source code, lowering the barrier to developing and contributing new workflows. QuAcc also streamlines the evaluation of foundation MLIPs by providing a unified platform for generating ab initio reference calculations consistent with the model of interest, mitigating methodological drift when assessing model performance. Together, these features make QuAcc a flexible and accessible framework for atomistic simulation workflows that have become central to the current era of machine learning and artificial intelligence.
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BibTeX
@misc{rosen2026democratizing,
title = {{Democratizing Atomistic Simulation Workflows for the AI Era with the Quantum Accelerator}},
author = {Andrew S. Rosen and Naisargi Goyal and Brad Ayers and Vineet Bansal and Julia H. Baratta and Samuel M. Blau and Yuan Chiang and Sihoon Choi and Orion Archer Cohen and Blake Dallmann and Tom Demeyere and Will Engler and Yue-Wen Fang and Isabella Furrick and Eliu Huerta and Honghui Kim and Hironori Kondo and Anup Kumar and Jaehong Kwon and Osman Mamun and Charles B. Musgrave III and Hananeh Oliaei and Aryan Saha and Davide Sarpa and Benjamin X. Shi and Yuliang Shi and Xing Wang and Robert B. Wexler},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.33823},
eprint = {2609.33823},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.33823},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Democratizing Atomistic Simulation Workflows for the AI Era with the Quantum Accelerator
AU - Andrew S. Rosen
AU - Naisargi Goyal
AU - Brad Ayers
AU - Vineet Bansal
AU - Julia H. Baratta
AU - Samuel M. Blau
AU - Yuan Chiang
AU - Sihoon Choi
AU - Orion Archer Cohen
AU - Blake Dallmann
AU - Tom Demeyere
AU - Will Engler
AU - Yue-Wen Fang
AU - Isabella Furrick
AU - Eliu Huerta
AU - Honghui Kim
AU - Hironori Kondo
AU - Anup Kumar
AU - Jaehong Kwon
AU - Osman Mamun
AU - Charles B. Musgrave III
AU - Hananeh Oliaei
AU - Aryan Saha
AU - Davide Sarpa
AU - Benjamin X. Shi
AU - Yuliang Shi
AU - Xing Wang
AU - Robert B. Wexler
PY - 2026
DA - 2026/09/27
PB - arXiv
DO - 10.48550/arxiv.2609.33823
UR - https://arxiv.org/abs/2609.33823
AB - We present the Quantum Accelerator (QuAcc), an open-source workflow library for atomistic simulations with an emphasis on quantum-mechanical calculations. QuAcc provides predefined workflow recipes spanning first-principles electronic-structure methods, semiempirical and tight-binding approaches, classical potentials, and foundation machine-learned interatomic potentials (MLIPs). A central design feature of QuAcc is its separation of domain-specific scientific logic from the workflow engine used to orchestrate and execute calculations. Workflows are written as ordinary Python functions and can be executed with multiple supported workflow engines without modifying the underlying source code, lowering the barrier to developing and contributing new workflows. QuAcc also streamlines the evaluation of foundation MLIPs by providing a unified platform for generating ab initio reference calculations consistent with the model of interest, mitigating methodological drift when assessing model performance. Together, these features make QuAcc a flexible and accessible framework for atomistic simulation workflows that have become central to the current era of machine learning and artificial intelligence.
ER -