Structure prediction of the solid forms of methanol: an ab initio random structure searching approach
文献信息
Tzu-Jen Lin, Cheng-Rong Hsing, Ching-Ming Wei, Jer-Lai Kuo
Liquid methanol and methanol clusters have been comprehensively studied to reveal their local structure and hydrogen bond networks. However, our understanding of the crystal forms of methanol is rather limited. The known crystal structures of solid methanol, α, β, and γ, are composed of infinite hydrogen bond chains in their unit cell. The structural diversity of solid methanol is much less than that of liquid methanol, in which both chain and ring structures exist and have been confirmed by experiments. In this study, we employed ab initio random structure searching (AIRSS) to study possible solid methanol structures. AIRSS predicted known solid methanol phases as well as various ring structures that have not been considered. A new possible candidate structure for the δ phase was also discovered. The relative stability of known solid methanol phases and our newly discovered structures were also investigated through dispersion corrected density functional theory. The density functional calculation provides reliable phase transition pressures between the known phases and the searched structures compared with experimental suggestions. In addition, the simulation result indicated that CH⋯O hydrogen bonds play a major role in stabilizing the methanol crystals under high pressures.
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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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