AI 中文总结
该研究提出适用于QUBO的高熵合金逆向设计主动学习框架，结合预训练图神经网络、二元变分自编码器与二次因式分解机，经基准测试可实现高效QUBO兼容的材料逆向设计。
AI 中文摘要
机器学习正向模型可快速预测合金属性，但在逆向设计中需同时兼容二次无约束二元优化（QUBO）时，其应用仍具挑战性。本文开发了一种适用于QUBO的高熵合金逆向设计主动学习框架，采用预训练图神经网络预测器作为固定属性 oracle；属性引导的二元变分自编码器提供二元潜在表示，二次因式分解机集成体则指导候选选择。通过受控潜在空间消融实验及与直接成分空间优化的对比，对该框架进行系统基准测试。结果表明，候选生成是决定搜索性能的关键因素：围绕先前高性能潜在代码的局部扰动可提供最大的工作流特定改进，而基于代理的选择进一步优先考虑富集搜索池中的候选。所得适用于QUBO的工作流与强大的经典优化策略相比仍具竞争力，尽管成分空间遗传算法取得了最高平均分数。最后，所学二次代理可直接导出为QUBO。这些结果表明，有效数据获取可与最终QUBO优化端点分离，为适用于QUBO的数据驱动材料逆向设计提供了基准化路径。
英文摘要
Machine-learned forward models can rapidly predict alloy properties, but their use for inverse design remains challenging when the search should also retain compatibility with quadratic unconstrained binary optimization (QUBO). Here, we develop a QUBO-compatible active-learning framework for inverse design of high-entropy alloys using a pretrained graph-neural-network predictor as a fixed property oracle. A property-guided binary variational autoencoder provides a binary latent representation, while an ensemble of quadratic factorization machines guides candidate selection. We systematically benchmark the framework through controlled latent-space ablations and comparison with direct composition-space optimization. The results show that candidate generation is a major determinant of search performance: local perturbations around previously high-performing latent codes provide the largest workflow-specific improvement, while surrogate-based selection further prioritizes candidates within the enriched search pool. The resulting QUBO-compatible workflow remains competitive with strong classical optimization strategies, although a composition-space genetic algorithm achieves the highest mean score. Finally, the learned quadratic surrogate can be exported directly as a QUBO. These results show that effective data acquisition can be separated from the final QUBO optimization endpoint, providing a benchmarked route for QUBO-compatible data-driven materials inverse design.
