Polymers of intrinsic microporosity (PIMs) are a family of materials with potential to be effective and scalable solutions for challenging adsorbent and membrane applications. The broad range of repeat unit chemistry, microporous structural features, and polymer processing makes exploration of the expansive PIM design space inefficient via chemical and materials intuition alone. Computational techniques such as molecular simulations and machine learning can provide a leap in capabilities to address this polymer design challenge and will be central to the future development of PIMs. We highlight recent microporous material studies that arrived at key results by employing computational techniques and provide our perspective on the prospects for in silico design and development of PIMs.

固有微孔聚合物 (PIM) 是一类材料，有可能成为具有挑战性的吸附剂和膜应用的有效且可扩展的解决方案。广泛的重复单元化学、微孔结构特征和聚合物加工使得仅通过化学和材料直觉探索广阔的 PIM 设计空间效率低下。分子模拟和机器学习等计算技术可以为解决这一聚合物设计挑战提供能力飞跃，并将成为 PIM 未来发展的核心。我们重点介绍了最近通过采用计算技术得出关键结果的微孔材料研究，并提供了我们对 PIM计算机设计和开发前景的看法。