用深度高斯过程优化材料设计，兼顾成本与效率
Deep Gaussian Process-based Cost-Aware Batch Bayesian Optimization for Complex Materials Design Campaigns
- 用多层高斯过程建模复杂材料特性间的层级关系
- 在相同迭代次数内找到更优配方，比传统方法更快收敛
- 适合资源有限的高温合金等复杂材料研发团队
材料发现速度加快、范围扩大，亟需高效优化框架以在庞大非线性设计空间中导航，并合理分配有限的评估资源。本文提出一种基于深度高斯过程（DGP）代理模型和异质查询策略的成本感知批量贝叶斯优化方法。该DGP通过堆叠多层高斯过程，建模高维成分特征间的复杂层次关系，捕捉多目标属性间的相关性，并逐层传递不确定性。我们将在上置信界获取函数中引入评估成本，结合异质查询策略，批量生成候选方案，平衡对未充分探索区域的探索与对高均值、低方差预测区的利用。应用于高温难熔高熵合金设计时，该框架在更少迭代次数内收敛至最优配方，显著优于传统基于高斯过程的贝叶斯优化，凸显深度、不确定性感知与成本敏感策略在材料研发中的价值。
原文摘要 · Abstract (English)
The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast, nonlinear design spaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns.
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