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Verified CAS / Academic Author2 Decoded Studies

Prof. LI Chaochao

Institute of Coal Chemistry, Chinese Academy of Sciences, State Key Laboratory of Coal Conversion, Taiyuan 030001, China

Research Publications & English Decoded Briefs

Showing 2 publications
New Carbon Materials2026DOI: 10.1016/S1872-5805(26)61099-2

A molecular dataset for the shear deformation of thermoplastic structural materials

The first molecular dynamics (MD) simulation dataset is reported for the interfacial shear behavior of carbon fiber/thermoplastic composites (CFRTPs), aimed at overcoming the critical interfacial problem that limits their high-end applications such as aerospace and new energy vehicles. The study features two key advances. First, we use the newly developed CHONSi-2024 reactive force field (ReaxFF), which provides high-precision parameters specifically for CFRTP interfacial systems. Second, the atomic models are constructed based on experimental characterization data and rigorously validated across multiple parameters, including shear modulus, yield behavior, stress-strain curves, and fracture morphology, ensuring quantitative agreement with experimental results. This dataset provides a complete record of the simulations, encompassing atomic trajectories, local structural changes, interfacial stress-strain responses, and system thermodynamic behaviors. These data offer direct atomic-scale insights into the interfacial strengthening mechanisms. The generated trajectories are compatible with mainstream software for visualization and analysis. Moreover, the dataset constitutes a high-quality resource for developing machine learning force fields, building structure-property relationships, and enabling the predictive modeling and high-throughput screening of composite interfaces. This dataset is anticipated to advance the fundamental understanding of composite interfaces and facilitate the rational design of high-performance CFRTPs.

Journal of Fuel Chemistry and Technology2026DOI: 10.1016/S1872-5813(26)60683-4

A Computational Dataset for C1 Molecular Catalytic Conversion over Iron-Based Catalysts

The catalytic conversion of C1 molecules (e.g., CO, CO2) is pivotal for sustainable C1 chemistry and low-carbon transformation. A profound understanding of microscopic reaction mechanisms requires systematic theoretical and experimental data. This study constructs a computational dataset for C1 molecular catalytic conversion over iron-based catalysts, focusing on Fe5C2 and systematically integrating multidimensional information on adsorption, dissociation, and formation reactions across crystal surfaces (001, 111, 510). The dataset comprises 690 directories and 1961 files, including 96 configurations for Fe5C2(001), 93 for Fe5C2(111), and 472 for Fe5C2(510). It covers adsorbed states (e.g., COH, H2), dissociated states (e.g., CO under 2H conditions), and formation states (e.g., CH/CH3 under 2H and H2O conditions). A standardized hierarchical storage system categorizes reaction types, crystal surfaces, and structural parameters. This dataset serves as a benchmark for validating quantum chemical methods and provides critical data support for catalyst design and reaction pathway optimization by uncovering coupling effects of crystal surfaces and reaction mechanisms. The data are publicly available via DOI: 10.57760/sciencedb.34259.