Kuang Yaming Honors School, Nanjing University, Nanjing, 210023, China.
Leibniz-Institut für Polymerforschung Dresden e.V., Hohe Strasse 6, 01069, Dresden, Germany.
Sci Rep. 2022 Aug 29;12(1):14664. doi: 10.1038/s41598-022-17938-x.
Design problems of finding efficient patterns, adaptation of complex molecules to external environments, affinity of molecules to specific targets, dynamic adaptive behavior of chemical systems, reconstruction of 3D structures from diffraction data are examples of difficult to solve optimal design or inverse search problems. Nature inspires evolution strategies to solve design problems that are based on selection of successful adaptations and heritable traits over generations. To exploit this strategy in the creation of new materials, a concept of adaptive chemistry was proposed to provide a route for synthesis of self-adapting molecules that can fit to their environment. We propose a computational method of an efficient exhaustive search exploiting massive parallelization on modern GPUs, which finds a solution for an inverse problem by solving repetitively a direct problem in the mean field approximation. One example is the search for a composition of a copolymer that allows the polymer to translocate through a lipid membrane at a minimal time. Another example is a search of a copolymer sequence that maximizes the polymer load in the micelle defined by the radial core-shell potentials. The length and the composition of the sequence are adjusted to fit into the restricted environment. Hydrogen bonding is another pathway of adaptation to the environment through reversible links. A linear polymer that interacts with water through hydrogen bonds adjusts the position of hydrogen bonds along the chain as a function of the concentration field around monomers. In the last example, branching of the molecules is adjusted to external fields, providing molecules with annealed topology, that can be flexibly changed by changing external conditions. The method can be generalized and applied to a broad spectrum of design problems in chemistry and physics, where adaptive behavior in multi-parameter space in response to environmental conditions lead to non-trivial patterns or molecule architectures and compositions. It can further be combined with machine learning or other optimization techniques to explore more efficiently the parameter space.
寻找有效模式、复杂分子对外界环境的适应、分子与特定目标的亲和力、化学系统的动态自适应行为、从衍射数据重建 3D 结构等设计问题是难以解决的最优设计或反向搜索问题的例子。自然启发了进化策略来解决基于选择成功适应和遗传特征的设计问题。为了在新材料的创造中利用这种策略，提出了自适应化学的概念，为合成能够适应环境的自适应分子提供了一种途径。我们提出了一种有效的穷举搜索计算方法，利用现代 GPU 的大规模并行化，通过在平均场近似中反复求解直接问题来找到反向问题的解。一个例子是搜索允许聚合物在最小时间内通过脂质膜迁移的共聚物组成。另一个例子是搜索共聚物序列，以最大限度地提高由径向核壳势定义的胶束中的聚合物负载。序列的长度和组成被调整以适应受限环境。氢键是通过可逆键适应环境的另一种途径。与水通过氢键相互作用的线性聚合物会根据单体周围的浓度场调整氢键在链上的位置。在最后一个例子中，分子的分支会根据外部场进行调整，为分子提供退火拓扑结构，通过改变外部条件可以灵活地改变拓扑结构。该方法可以推广并应用于化学和物理领域的广泛设计问题，其中多参数空间中的自适应行为对环境条件的响应会导致非平凡的模式或分子结构和组成。它还可以与机器学习或其他优化技术结合使用，以更有效地探索参数空间。
