Stochastic atomic modeling and optimization with fullrmc
B Aoun, JOURNAL OF APPLIED CRYSTALLOGRAPHY, 55, 1664-1676 (2022).
Understanding materials' atomic structure with a high level of confidence and certainty is often regarded as a very arduous and sometimes impossible task, especially for newer, emerging technology materials exhibiting limited long-range order. Nevertheless, information about atomic structural properties is very valuable for materials science and synthesis. For non-crystalline amorphous and nanoscale materials, using conventional structural determination methods is impossible. Reverse Monte Carlo (RMC) modeling is commonly used to derive models of materials from experimental diffraction data. Here, the latest developments in the fullrmc software package are discussed. Despite its name, fullrmc provides a very flexible modeling framework for solving atomic structures with many methods beyond RMC. The stochastic nature of fullrmc allows it to explore all possible dimensions and degrees of freedom for atomic modeling and create statistical solutions to match measurements. Differing versions of fullrmc are provided as open source or for cloud computing access. The latter includes a modern web-based graphical user interface that incorporates advanced computing and structure-building modules and machine-learning-based components. The main features of fullrmc are presented, including constraint types, boundary conditions, density shape functions and the two running modes: stochastic using a Monte Carlo algorithm and optimization using a genetic algorithm. Capabilities include tools for statistical, mesoscopic and nanoscopic approaches, atomic or coarse-grained models, and smart artificial-intelligence-ready loss functions.
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