Substantial acceleration of research and more efficient utilization of resources can be achieved in modelling investigated phenomena by identifying the limits of system's accessible states instead of tracing the trajectory of its evolution. The proposed strategy uses the Metropolis-Hastings Monte-Carlo sampling of the configuration space probability distribution coupled with physically-motivated prior probability distribution. We demonstrate this general idea by presenting a high performance method of generating configurations for lattice dynamics and other computational solid state physics calculations corresponding to non-zero temperatures. In contrast to the methods based on molecular dynamics, where only a small fraction of obtained data is used, the proposed scheme is distinguished by a considerably higher, reaching even 80%, acceptance ratio and much lower amount of computation required to obtain adequate sampling of the system in thermal equilibrium at non-zero temperature.

中文翻译：

---

高效配置空间采样——探测可用状态的分布

通过识别系统可访问状态的限制而不是追踪其演化轨迹，可以在对所研究现象进行建模中实现研究的显着加速和资源的更有效利用。所提出的策略使用配置空间概率分布的 Metropolis-Hastings Monte-Carlo 采样以及物理激励的先验概率分布。我们通过提出一种为晶格动力学和其他与非零温度对应的计算固态物理计算生成配置的高性能方法来证明这一总体思路。与基于分子动力学的方法相比，仅使用一小部分获得的数据，所提出的方案的特点是相当高，甚至达到 80%，