SANE：用于多模态非可微黑盒函数中多最优解发现的战略自主非光滑探索
SANE: Strategic Autonomous Non-Smooth Exploration for Multiple Optima Discovery in Multi-modal and Non-differentiable Black-box Functions
AI总结：
SANE提出成本驱动概率采集函数与动态代理门控，在噪声多模态黑盒函数中高效探索多个最优区域，优于经典贝叶斯优化，助力自主实验科学发现。
摘要：
计算和实验材料发现都带来了探索多维和多模态参数空间的挑战，例如具有多种相互作用的哈密顿量的相图、组合库的成分空间、材料结构图像空间和分子嵌入空间。这些系统通常是黑盒且评估耗时，这导致了对贝叶斯优化（BO）等主动学习方法的强烈兴趣。然而，这些系统通常带有噪声，使得黑盒函数严重多模态且不可微，此时标准贝叶斯优化可能过度聚焦于单一或虚假的最优解，偏离了科学发现的更广泛目标。为解决这些局限性，我们开发了战略自主非光滑探索（SANE），以促进智能贝叶斯优化导航，通过提出的成本驱动概率采集函数来发现多个全局和局部最优区域，避免陷入单一最优解的倾向。为区分因噪声实验测量导致的真实与虚假最优区域，我们将人类（领域）知识驱动的动态代理门控集成到SANE中。我们将门控SANE应用于预先获取的具有高噪声水平的特定区域的铁电组合库的压电响应谱数据，以及压电响应力显微镜（PFM）高光谱数据。SANE展示了优于经典贝叶斯优化的性能，促进了多个最优区域的探索，从而在自主实验中优先学习，覆盖了更高的科学价值。我们的工作展示了该方法在实际实验中的潜在应用，在这种结合战略和人工干预的方法对于在自主研究中解锁新发现可能至关重要。
英文摘要：
Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-box and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into a pre-acquired Piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and a piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiment, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.
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