Many materials and chemical systems exhibit history-dependent responses, where functional outcomes are governed not only by final-state variables but by the time-dependent sequence of fields, temperatures, or chemical potentials applied during operation. Discovering new processing protocols is therefore a high-dimensional search problem in which the control variable is an entire waveform or sample history, and conventional strategies either remain confined to conservative interpolative families or become prohibitively measurement intensive. Here, a closed-loop workflow is introduced that couples evolutionary search over a compact waveform representation with uncertainty-aware deep kernel learning to generate, rank, and experimentally validate candidate protocols. Applied to ferroelectric thin films, with the scanning-probe tip-bias waveform as the protocol and the nonlinear electromechanical response as the reward, the workflow discovers waveform families that enhance nonlinearity by de-aging the film. Spatially resolved before/after measurements show that the best-performing waveforms selectively activate pre-existing, weakly pinned domain-wall segments, whereas the worst drive long-range irreversible switching. This framework reframes protocol tuning as out-of-distribution discovery, generalizable to synthesis and annealing trajectories, battery formation protocols, and other high-dimensional control problems.

翻译：许多材料和化学系统表现出历史依赖的响应行为，其中功能结果不仅取决于最终状态变量，还取决于操作过程中施加的场、温度或化学势的时间序列。因此，发现新型处理协议本质上是一个高维搜索问题，其中控制变量是整个波形或样本历史，而传统策略要么局限于保守的内插族，要么因测量强度过高而不可行。本文提出了一种闭环工作流程，将紧凑波形表示上的进化搜索与不确定性感知深度核学习相结合，以生成、排序并实验验证候选协议。应用于铁电薄膜时，以扫描探针尖端偏压波形作为协议、非线性机电响应作为奖励，该工作流程发现了通过去老化效应增强非线性的波形族。空间分辨的前后测量表明，性能最佳的波形选择性激活了预先存在的弱钉扎畴壁段，而最差的波形则驱动了长程不可逆切换。该框架将协议调优重新定义为离群发现范式，可推广至合成与退火轨迹、电池形成协议及其他高维控制问题。