A modelling algorithm for amorphous covalent triazine-based polymers
Literature Information
Fangyuan Hu, Zhaoliang Meng, Shengming Li, Tianpeng Zhang, Wenlong Shao, Siyang Liu
Rational and purposeful designs of amorphous materials with desirable structures are difficult to implement due to the complex and unordered nature of such materials. In this work, a modelling algorithm was proposed for amorphous covalent triazine-based polymers to construct atomistic representative models that can reproduce the experimentally measured properties of experimental samples. The constructed models were examined through comparisons of simulated and experimental properties, such as surface area, pore volume, and structure factor, and further validated by the good consistency observed among these properties. To assess the predictive capability of the modelling algorithm, we used a new covalent triazine-based polymer and predicted its porosity by constructing a simulated model. The predicted results on the surface area and pore volume of the simulated model were quantitatively consistent with the experimental data derived from the experimentally synthesized sample. This consistency reveals the predictive capacity of the proposed modelling algorithm. The algorithm could be a promising approach to predict and develop advanced covalent triazine-based polymers for multiple applications.
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Source Journal
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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