Successful innovations in materials have the potential to revolutionize society. However, traditional material research often faces lengthy timelines and low success rates, which deters investors’ interest in seeking quicker returns on investments. Integrating the emerging simulation technologies has the potential to accelerate the pace of material development, along with experimental efforts. High-performance computing methods play a crucial role by focusing on experimental results to generate promising compounds through solving predictive models. Machine learning serves as a bridge between the existing technologies, facilitating the refinement of theoretical models based on experimental data and guiding future experiments. The use of mathematical tools will fundamentally transform the landscape of materials research over the next few decades. Therefore, successful implementation of the simulation tools and synthesis methods pushes forward the development of high-performance materials for structural and functional applications in various engineering sectors.

The integration of high-performance computing, modeling, and machine learning holds a great promise for accelerating materials discovery. Recent advancements in theory, high-throughput materials synthesis, diagnostics, defect understanding, and machine learning are driving this transformation forward. By leveraging existing methods for materials development, adopting data standards, deploying data management tools, and enhancing laboratory feedback cycles, new insights and synthesis ways can be unlocked to prepare high performance materials, include epoxy, polyimide and many others. Computational science plays a pivotal role in elucidating microscopic properties that are challenging to observe experimentally. By refining the physical and mathematical models across multiple scales, along with integrating polymer informatics, the access to rich data insights that inform the design and selection of materials for diverse environments can be achieved.

Papers relating to the analysis of molecular or sub-nano structure of materials, either experimentally or theoretically, are relevant to the topic of this issue. Specific topics include the use of simulation tools such as LAMMPS, Gromacs, CP2L, xtb, Quantum Espresso, VASP, ORCA, machine learning and specially designed experiments with the aid of mathematical models are particularly welcome. The submission covering the following areas is also within the scope of the proposed field.

1. The relationship among polymer molecules, reaction, and sub-nano structure.

2. The active mechanism of functional materials study by computing-aid or experiment tools.

3. Screening of high-performance polymer candidates and the prediction of properties.

4. Development of an experimental and simulation database for polymers.

5. Precise polymer all-atom modeling in molecular dynamics.

6. Development of advanced computational and simulation methods.

7. The preparation and characterization of high-performance materials and related composites.

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Frontiers in Materials

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