Flat-histogram extrapolation as a useful tool in the age of big data
Flat-histogram extrapolation as a useful tool in the age of big data
复制标题
平坦直方图外推法是大数据时代的有用工具
DOI：
10.1080/08927022.2020.1747617
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发表时间：
2020-04-14
影响因子：
2.1
通讯作者：
Shen, Vincent K.
中科院分区：
文献类型：
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作者：
Mahynski, Nathan A.;Hatch, Harold W.;Shen, Vincent K.
在这里，我们回顾了作者最近的工作，重新使用统计力学原理外推经典系统的热力学性质的概念。具体来说，我们讨论如何结合这些原则与偏置采样技术，使自由能景观和其他详细信息，如结构特性，系统的预测问题。使用这种方法已经在广泛的条件下实现了对物理特性的非常准确的估计，大大减少了探索给定系统行为所需的模拟数量。虽然近似，但这些外推显着放大了可以从模拟中提取的合理准确的信息量，使其中一小部分能够为神经网络等数据密集型回归算法提供数据。因此，这种外推方法代表了一种有用的工具，用于执行诸如高通量筛选物理特性、优化力场参数、探索平衡相行为以及为这些系统提供理论指导的数据科学等任务。
Here we review recent work by the authors to revisit the concept of extrapolating thermodynamic properties of classical systems using statistical mechanical principles. Specifically, we discuss how the combination of these principles with biased sampling techniques enables the prediction of free energy landscapes and other detailed information, such as structural properties, of the system in question. Remarkably accurate estimates of physical properties across a broad range of conditions have been achieved using this approach, greatly reducing the number of simulations needed to explore a given system's behaviour. While approximate, these extrapolations significantly amplify the amount of reasonably accurate information that can be extracted from simulations enabling a small set of them to feed data-intensive regression algorithms such as neural networks. Thus, this extrapolation methodology represents a useful tool for performing tasks such as high-throughput screening of physical properties, optimising force field parameters, exploring equilibrium phase behaviour, and enabling theory-guided data science for these systems.