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arXiv 1306.1812physics.chem-phcond-mat.mtrl-scistat.ML
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通过机器学习实现无轨道键断裂

Orbital-free Bond Breaking via Machine Learning

John C. Snyder, Matthias Rupp, Katja Hansen, Leo Blooston, Klaus-Robert Müller, Kieron Burke

摘要:

机器学习用于将一维电子的动能近似为电子密度的函数。该功能可以准确地解离双原子,并可以通过训练系统地提高。高精度的自洽密度和分子力的发现,表明从头计算分子动力学模拟的可能性。

英文摘要:

Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic, and can be systematically improved with training. Highly accurate self-consistent densities and molecular forces are found, indicating the possibility for ab-initio molecular dynamics simulations.

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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