标题： 基于机器学习的DNA功能化纳米颗粒多尺度设计用于双螺旋结构的靶向自组装 显示英文标题

标题： Machine Learning Guided Multiscale Design of DNA-functionalized Nanoparticles for Targeted Self-Assembly of the Double Gyroid

摘要： 在软物质科学中，通常的目标是设计具有特定性质的新材料。这些材料可以用于许多应用，每种应用都需要优化特定的特性以达到最大的适配性。利用自组装过程，其中目标结构是从适当设计的构建模块中获得的，是这一优化的强大工具。然而，如何一般性地设计可实现的构建模块以导致所需的相态，仍然是一个开放性问题。在这项工作中，双螺旋结构被选为目标结构，使用DNA功能化的纳米颗粒作为我们的构建模块。利用现有的对势作为灵感，定义了一个大的设计空间以探索目标结构。实施了一种有效的搜索策略，首先使用自由能计算对原始的细粒度构建模块模型进行粗粒化，然后快速评估每个设计的适配性。这些数据随后被输入到机器学习算法中，通过主动学习循环获得我们设计空间中所有候选人的预测。通过与细粒度模型的界面钉扎计算，成功粗粒化的设计被再次识别和评估。这项工作导致了特定的、实验相关的DNA功能化纳米颗粒设计的发展，这些设计能够自组装成目标相态。所使用的方法可以扩展到其他类型的构建模块和目标结构。 显示英文摘要

摘要： In soft matter science, it is often the goal to design new materials with targeted properties. These materials can be used in many applications, each requiring specific features to be optimized for maximum fitness. The use of self-assembly processes, where a target structure is attained from suitably designed building blocks, is a powerful tool for this optimization. However, it is still an open question how to generally design realizable building blocks that lead to the desired phases. In this work, the double gyroid is chosen as target structure and DNA functionalized nanoparticles are used as our building blocks. Using existing pair potentials as inspiration, a large design space is defined for exploration of the target structure. An effective search strategy is implemented, where free energy calculations are first used to coarse grain our originally fine-grained model of the building blocks and then quickly evaluate the fitness of each design. These data are then fed into a machine learning algorithm that allows obtaining predictions for all candidates in our design space through an active learning loop. Successful coarse-grained designs are identified and evaluated again through interfacial pinning calculations with the fine-grained model. This work leads to the development of specific, experimentally relevant designs of DNA functionalized nanoparticles that self-assembly into the target phase. The methodology used can be extended to other types of building blocks and target structures.