用物理数据驱动模型优化二氧化碳电极，提升自动化实验效率。
A physics-based data-driven model for CO$_2$ gas diffusion electrodes to drive automated laboratories
- 结合物理模型与实验数据，构建可解释的不确定性感知框架。
- 在真实电化学数据上验证，显著提升多维参数空间探索效率。
- 适合从事电催化、自动化实验或材料设计的研究者参考。
将大气中二氧化碳通过可再生能源进行电化学还原，转化为高能量分子，是利用现有基础设施实现能源存储的有前景途径，尤其适用于缺乏可持续替代品的地区。当前自动化实验室正被开发用于优化气体扩散电极（GDE）的组成与运行条件，而提升其效率对技术可行性至关重要。本文提出一种建模框架，在主动学习背景下高效探索高维的GDE设计参数空间。核心是一个经过实验数据校准的不确定性感知物理模型，具备捕捉多种输入参数空间及任何符合塔菲尔动力学的碳产物的能力。该模型具有可解释性，其高斯过程层可捕获真实数据与物理模型函数空间之间的偏差。我们在模拟的主动学习设置中部署该模型，并使用AdaCarbon自动化实验室获取的真实电化学数据进行测试，结果表明其能高效遍历多维参数空间。
原文摘要 · Abstract (English)
The electrochemical reduction of atmospheric CO$_2$ into high-energy molecules with renewable energy is a promising avenue for energy storage that can take advantage of existing infrastructure especially in areas where sustainable alternatives to fossil fuels do not exist. Automated laboratories are currently being developed and used to optimize the composition and operating conditions of gas diffusion electrodes (GDEs), the device in which this reaction takes place. Improving the efficiency of GDEs is crucial for this technology to become viable. Here we present a modeling framework to efficiently explore the high-dimensional parameter space of GDE designs in an active learning context. At the core of the framework is an uncertainty-aware physics model calibrated with experimental data. The model has the flexibility to capture various input parameter spaces and any carbon products which can be modeled with Tafel kinetics. It is interpretable, and a Gaussian process layer can capture deviations of real data from the function space of the physical model itself. We deploy the model in a simulated active learning setup with real electrochemical data gathered by the AdaCarbon automated laboratory and show that it can be used to efficiently traverse the multi-dimensional parameter space.
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