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博尔茨曼发生器:深度学习的多体系统的样本平衡状态

Frank Noé^1,2,3, Simon Olsson⁴, Jonas Köhler⁴

¹FU Berlin, Department of Mathematics and Computer Science, Arnimallee 6, 14195 Berlin, Germany. frank.noe@fu-berlin.de.

Science (New York, N.Y.)

|September 7, 2019

概括

我们开发了博尔茨曼生成器, 这是一种深度学习方法, 这种方法显著降低了模拟凝聚物质和生物系统的计算成本.

科学领域:

计算物理
统计力学
机器学习

背景情况:

在诸如蛋白质的多体系统中模拟平衡状态是计算密集的.
目前的方法如分子动力学需要小的,连续的步骤, 需要大量的计算资源.
在单个步骤中生成统计学上独立的平衡样本仍然是一个挑战.

研究的目的:

开发一种新的计算方法,有效地生成复杂系统的无偏平衡样本.
将深度学习与统计力学结合起来,克服当前模拟技术的局限性.
为了实现准确的自由能量计算和发现新的配置.

主要方法:

开发了一个深度学习框架.
使用神经网络来学习配置分布的坐标转换.
将复杂的平衡分布转化为容易取样的分布.
将该方法应用于凝聚物质系统和蛋白质.

主要成果:

波兹曼发电机成功地产生了无偏见的,一次性的平衡样本.
证明了自由能量差异的准确计算.
展示了新型结构的发现.
这种方法有效地避免了在采样过程中发生的罕见事件.

结论:

波兹曼发生器为统计力学模拟提供了一个强大的新工具.
这种方法显著提高了复杂系统中计算平衡状态的效率.
该方法在凝聚物质物理学,化学和结构生物学方面具有广泛的应用.

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