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arXiv 2202.09186cond-mat.mtrl-sci
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物质电子结构神经网络的免训练超参数优化

Training-free hyperparameter optimization of neural networks for electronic structures in matter

Lenz Fiedler, Nils Hoffmann, Parvez Mohammed, Gabriel A. Popoola, Tamar Yovell, Vladyslav Oles, J. Austin Ellis, Siva Rajamanickam, Attila Cangi

摘要:

材料科学和化学中的无数现象都依赖于对物质电子结构的量子级模拟。虽然几十年来,向更大的长度和时间尺度移动一直是一个紧迫的问题,但尽管现代软件方法和高性能计算的进步,这种大规模的电子结构计算仍然具有挑战性。在这方面的一线希望是使用机器学习来加速电子结构计算-这一研究领域最近受到越来越多的关注。其中最大的挑战是在称为超参数优化的过程中找到合适的机器学习模型。然而,除了数据生成的开销之外,这还导致了大量的计算开销。我们通过在超参数优化阶段避免过度训练,将神经网络模型的构建速度加快了大约两个数量级。我们展示了我们的工作流程Kohn-Sham密度泛函理论,最流行的计算方法在材料科学和化学。

英文摘要:

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations -- this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of neural network models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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