AI 中文总结
本研究采用SE(3)等变Transformer作为机器学习代理集体变量，采样离子胶体晶体系统的自由能景观，实现了对亚稳态的区分及晶体结构偏好的调控。
AI 中文摘要
涂有聚合物刷的带电胶体可通过调节易控实验条件，优先自组装成不同晶体结构。在给定条件下，实验与模拟均观测到热力学（亚）稳定自组装晶体结构的分布。准确量化这些胶体系统的自由能景观，对合理选择条件以优先获得特定晶体结构至关重要。对于部分形成的结构，标准晶体序参数无法区分不同晶体，也无法区分晶体与无定形聚集体。研究表明，局部环境相似性描述符可区分相关亚稳态，但用于偏置分子动力学（MD）模拟时成本过高。本文采用机器学习相互作用势领域的方法，证明SE(3)等变Transformer网络可作为易评估的机器学习代理，从而可计算可及胶体结构的相对自由能，该自由能随不同实验相关物理旋钮变化，这些旋钮可引导系统在两种观测晶体类型间转换。作为示例应用，本文展示了在保持相同吸引能的同时改变正、负胶体的表面电势，可改变哪种晶体结构更受青睐。
英文摘要
Charged colloids coated with a polymer brush can be designed to preferentially self-assemble into different crystal structures by varying easy-to-tune experimental conditions. For a given set of conditions, we have observed in experiments and simulations a distribution of thermodynamically (meta)stable self-assembled crystal structures. Properly quantifying the free energy landscape of these colloidal systems is essential for rationally choosing conditions to preferentially target particular crystal structures. For some of the structures we have formed, standard crystalline order parameters are not able to differentiate between crystals or between crystals and amorphous aggregates. We show that local environment similarity descriptors are able to distinguish the relevant metastable states, but are too expensive for use in biased MD simulations. Here, we adopt an approach from machine-learned interaction potentials showing that SE(3)-equivariant transformer networks can serve as an efficient-to-evaluate machine-learned proxy. As a result, we can compute the relative free energies of accessible colloidal structures as a function of different experimentally-relevant physical knobs that can steer our system between two observed crystal types. As an example application, we then show how changing surface potentials of positive and negative colloids while maintaining the same attractive energy can shift which crystal structure is favored.
Comments10 pages, 5 figures
