The method of molecular dynamics (MD) is a powerful tool for the predictionudand investigation of various phenomena in physics, chemistry and biology.udThe development of efficient MD algorithms for integration of the equationsudof motion in classical and quantum many-body systems should thereforeudimpact a lot of fields of fundamental research. In the present study it isudshown that most of the existing MD integrators are far from being ideal andudfurther significant improvement in the efficiency of the calculations can beudreached. As a result, we propose new optimized algorithms which allowudto reduce the numerical uncertainties to a minimum with the same overalludcomputational costs. The optimization is performed within the well recognizeduddecomposition approach and concerns the widely used symplecticudVerlet-, Forest-Ruth-, Suzuki- as well as force-gradient-based schemes. Itudis concluded that the efficiency of the new algorithms can be achievedudbetter with respect to the original integrators in factors from 3 to 1000 forudorders from 2 to 12. This conclusion is confirmed in our MD simulationsudof a Lennard-Jones fluid for a particular case of second- and fourth-orderudintegration schemes.
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机译：分子动力学方法（MD）是预测，研究和研究物理，化学和生物学中各种现象的有力工具。 ud开发了有效的MD算法，用于将经典方程和量子多体中的方程 udof运动进行积分因此，系统应该影响许多基础研究领域。在本研究中， u n表明大多数现有的MD集成器远非理想状态，未达到计算效率的显着提高。结果，我们提出了一种新的优化算法，该算法可以以相同的总体计算成本将数值不确定性降低到最小。该优化是在公认的 uddecomposition方法中执行的，涉及广泛使用的辛 udVerlet，Forest-Ruth，Suzuki和基于力梯度的方案。它的结论是，相对于原始积分器，新算法的效率可以达到3到1000的因数，从2到12的因数可以得到更好的结果。这一结论在Lennard-Jones的MD仿真中得到了证实。对于二阶和四阶 udintegration方案的特定情况，流体。
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