A simple bulk modulus model for crystal materials based on the bond valence model
文献信息
Xiao Liu, Hao Wang, Weimin Wang, Zhengyi Fu
An empirical model based on the bond valence model has been presented to predict the bulk modulus of crystal materials. The bond bulk modulus, in terms of bond valences and bond length, is introduced to describe the resisting ability of a chemical bond to compression. Both the bond bulk modulus and bond density are proposed to be the most important parameters related to the bulk modulus of crystals. For typical ANB8−N and AmBn type crystals, the calculated bulk modulus is in good agreement with experimental values. For multibond crystal systems, the bulk modulus can be equivalent to an average of the bulk modulus of all constituted binary systems. Applied to spinel, B–C–N, polymorphic AR2O4, and chalcopyrite type multibond compounds, their calculated bulk moduli agree well with the available experimental results. Our bulk modulus model can offer a simple and reliable prediction based on the well-described nature of chemical bonding, which makes it powerful for extensively exploring novel superhard materials with low compressibility, and for interpreting geophysical problems.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.












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