We describe a method to explore the configurational phase space of chemical systems. It is based on the nested sampling algorithm recently proposed by Skilling [Skilling J. (2004) In AIP Conference Proceedings, vol. 735, p. 395.; Skilling J. (2006) J of Bayesian Analysis 1:833-860.] and allows us to explore the entire potential energy surface (PES) efficiently in an unbiased way. The algorithm has two parameters which directly control the trade-off between the resolution with which the space is explored and the computational cost. We demonstrate the use of nested sampling on Lennard-Jones (LJ) clusters. Nested sampling provides a straightforward approximation for the partition function, thus evaluating expectation values of arbitrary smooth operators at arbitrary temperatures becomes a simple post-processing step. Access to absolute free energies allows us to determine the temperature-density phase diagram for LJ cluster stability. Even for relatively small clusters, the efficiency gain over parallel tempering in calculating the heat capacity is more than an order of magnitude. Furthermore, by analysing the topology of the resulting samples we are able to visualise the PES in a new and illuminating way. We identify a discretely valued order parameter with basins and supra-basins of the PES allowing a straightforward and unambiguous definition of macroscopic states of an atomistic system and the evaluation of the associated free energies.
Configurational-space separation and structure selection in three hard squares
Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education and School of Physics and Technology, Wuhan University（教育部人工微纳结构重点实验室与物理科学与技术学院，武汉大学）；Wuhan Institute of Quantum Technology（武汉量子技术研究院）
Automatic Refinement of Force Fields Based on Phase Diagrams
Peking University（北京大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；ByteDance Seed - AI for Science, Shenzhen Bay Technology Innovation Center（字节跳动种子-科学智能，深圳湾科技创新中心）
