用贝叶斯优化自动寻找最佳合金设计问题，加速材料研发
Towards Autonomous Experimentation: Bayesian Optimization over Problem Formulation Space for Accelerated Alloy Development
- 在问题表述空间中用贝叶斯优化搜索最优设计目标组合
- 在模拟钼铌钛钒钨合金中找到满足强度、密度等阈值的最优配方
- 适合需要多目标权衡的材料自主设计场景
材料科学中的加速发现依赖于能动态提出并求解设计问题的自主系统。本文提出一种新框架，通过在问题表述空间中进行贝叶斯优化，识别与决策者偏好一致的最优设计表述。通过将不同设计场景映射到多属性效用函数，该方法可在无需初始精确定义问题的情况下，平衡延展性、屈服强度、密度和凝固范围等冲突目标。我们在面向燃气轮机叶片应用的Mo-Nb-Ti-V-W合金体系中开展仿真案例研究，框架收敛至满足关键性能阈值的最优方案，表明将问题表述发现纳入自主设计循环可显著简化实验流程。未来工作将引入人类反馈，进一步提升系统在真实实验环境中的适应能力。
原文摘要 · Abstract (English)
Accelerated discovery in materials science demands autonomous systems capable of dynamically formulating and solving design problems. In this work, we introduce a novel framework that leverages Bayesian optimization over a problem formulation space to identify optimal design formulations in line with decision-maker preferences. By mapping various design scenarios to a multi attribute utility function, our approach enables the system to balance conflicting objectives such as ductility, yield strength, density, and solidification range without requiring an exact problem definition at the outset. We demonstrate the efficacy of our method through an in silico case study on a Mo-Nb-Ti-V-W alloy system targeted for gas turbine engine blade applications. The framework converges on a sweet spot that satisfies critical performance thresholds, illustrating that integrating problem formulation discovery into the autonomous design loop can significantly streamline the experimental process. Future work will incorporate human feedback to further enhance the adaptability of the system in real-world experimental settings.
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