Chen Pengyu, Dorfman Kevin D
Department of Chemical Engineering and Materials Science, University of Minnesota-Twin Cities, Minneapolis, MN 55455.
Proc Natl Acad Sci U S A. 2023 Nov 7;120(45):e2308698120. doi: 10.1073/pnas.2308698120. Epub 2023 Nov 3.
Block polymers are an attractive platform for uncovering the factors that give rise to self-assembly in soft matter owing to their relatively simple thermodynamic description, as captured in self-consistent field theory (SCFT). SCFT historically has found great success explaining experimental data, allowing one to construct phase diagrams from a set of candidate phases, and there is now strong interest in deploying SCFT as a screening tool to guide experimental design. However, using SCFT for phase discovery leads to a conundrum: How does one discover a new morphology if the set of candidate phases needs to be specified in advance? This long-standing challenge was surmounted by training a deep convolutional generative adversarial network (GAN) with trajectories from converged SCFT solutions, and then deploying the GAN to generate input fields for subsequent SCFT calculations. The power of this approach is demonstrated for network phase formation in neat diblock copolymer melts via SCFT. A training set of only five networks produced 349 candidate phases spanning known and previously unexplored morphologies, including a chiral network. This computational pipeline, constructed here entirely from open-source codes, should find widespread application in block polymer phase discovery and other forms of soft matter.
嵌段聚合物是一个极具吸引力的平台，用于揭示在软物质中引发自组装的因素，这是因为它们具有相对简单的热力学描述，正如自洽场理论（SCFT）所体现的那样。从历史上看，SCFT在解释实验数据方面取得了巨大成功，能够让人们从一组候选相中构建相图，并且现在人们对将SCFT用作筛选工具来指导实验设计有着浓厚的兴趣。然而，使用SCFT进行相发现会导致一个难题：如果需要预先指定候选相的集合，那么如何发现一种新的形态呢？通过用来自收敛的SCFT解的轨迹训练一个深度卷积生成对抗网络（GAN），然后部署该GAN来生成用于后续SCFT计算的输入场，这一长期存在的挑战得以克服。通过SCFT展示了这种方法对于纯二嵌段共聚物熔体中网络相形成的强大作用。仅由五个网络组成的训练集产生了349个候选相，涵盖了已知的和以前未探索过的形态，包括一种手性网络。这里完全由开源代码构建的这个计算流程，应该会在嵌段聚合物相发现和其他形式的软物质中得到广泛应用。
