AI 中文总结
本研究开发了QCE和AQCE密度泛函，用于预测嵌段共聚物域结构，显著降低周期和轮廓误差，并准确再现相图拓扑，且可扩展至其他体系结构。
AI 中文摘要
相场模型为自洽场理论（SCFT）提供了一种高效替代方案，但嵌段共聚物相行为的定量预测仍具挑战性。我们开发了一种二次连通熵（QCE）密度泛函及其自适应扩展（AQCE）。QCE将二次非局域连通性与非线性二元相对熵项相结合，而AQCE增加了有界界面刚度，同时保持均匀响应。在层状基准测试中，QCE在测试模型中给出了最低的平均轮廓误差，AQCE给出了最低的平均周期误差。与拟合SCFT力和应力数据的优化相场（OPF）模型相比，AQCE将强分离周期和轮廓误差分别降低了五倍以上和六倍以上。AQCE进一步再现了AB二嵌段共聚物SCFT相图的拓扑结构，这是对竞争形态自由能排序的更严格测试。由于体系结构通过理想链相关性进入，相同的非线性构造可扩展到其他A/B体系结构。我们展示了这种转移对于对称线性ABA层状结构，准确预测了它们的轮廓、周期和有序化阈值。
英文摘要
Phase-field models offer an efficient alternative to self-consistent field theory (SCFT), but quantitative predictions of block-copolymer phase behavior remain challenging. We develop a quadratic-connectivity entropic (QCE) density functional and its adaptive extension (AQCE). QCE combines quadratic nonlocal connectivity with a nonlinear binary relative-entropy term, while AQCE adds bounded interfacial stiffness that preserves the homogeneous response. In lamellar benchmarks, QCE gives the lowest mean profile error among the tested models and AQCE the lowest mean period error. Compared with the optimized phase-field (OPF) model fitted to SCFT force and stress data, AQCE reduces strong-segregation period and profile errors by more than fivefold and sixfold, respectively. AQCE further reproduces the topology of the SCFT phase diagram for AB diblocks, a more demanding test of free-energy rankings among competing morphologies. Because architecture enters through ideal-chain correlations, the same nonlinear construction extends to other A/B architectures. We demonstrate this transfer for symmetric linear ABA lamellae, accurately predicting their profiles, periods, and ordering threshold.
