Jiao Sally, Shell M Scott
Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA.
J Chem Phys. 2024 Mar 28;160(12). doi: 10.1063/5.0200900.
Design of next-generation membranes requires a nanoscopic understanding of the effect of biologically inspired heterogeneous surface chemistries and topologies (roughness) on local water and solute behavior. In particular, the rejection of small, neutral solutes, such as boric acid, poses a heretofore unsolved challenge. In prior work, a computational inverse design technique using an evolutionary optimization successfully uncovered new surface design strategies for optimized transport of water over solutes in smooth, model pores consisting of two surface chemistries. However, extending such an approach to more complex (and realistic) scenarios involving many surface chemistries as well as surface roughness is challenging due to the expanded design space. In this work, we develop a new approach that uses active learning to optimize in a reduced feature space of surface group interactions, finding parameters that lead to their assembly into ordered, optimal patterns. This approach rapidly identifies novel surface functionalizations that maximize the difference in water and boric acid transport through the nanopore. Moreover, we find that the roughness of the nanopore wall, independent of its chemistry, can be leveraged to enhance transport selectivity: oscillations in the pore wall diameter optimally inhibit boric acid transport by creating energetic wells from which the solute must escape to transport down the pore. This proof-of-concept demonstrates the potential for active learning strategies, in concert with molecular simulations, to rapidly navigate complex design spaces of aqueous interfaces and is promising as a tool for engineering water-mediated surface interactions for a broad range of applications.
下一代膜的设计需要从纳米尺度理解受生物启发的异质表面化学和拓扑结构（粗糙度）对局部水和溶质行为的影响。特别是，对硼酸等小的中性溶质的截留构成了一个迄今尚未解决的挑战。在之前的工作中，一种使用进化优化的计算逆向设计技术成功地发现了新的表面设计策略，用于在由两种表面化学组成的光滑模型孔中实现水相对于溶质的优化传输。然而，由于设计空间的扩大，将这种方法扩展到涉及多种表面化学以及表面粗糙度的更复杂（和现实）的情况具有挑战性。在这项工作中，我们开发了一种新方法，该方法使用主动学习在表面基团相互作用的简化特征空间中进行优化，找到能使它们组装成有序、最优模式的参数。这种方法能快速识别出新型表面功能化结构，从而使通过纳米孔的水和硼酸传输差异最大化。此外，我们发现纳米孔壁的粗糙度，与其化学性质无关，可以用来提高传输选择性：孔壁直径的振荡通过形成溶质必须从中逃脱才能沿孔传输的能量阱，最优地抑制了硼酸的传输。这一概念验证证明了主动学习策略与分子模拟协同作用，在快速探索水相界面复杂设计空间方面的潜力，并且有望成为一种用于设计广泛应用中与水介导的表面相互作用相关工程的工具。
