Manufacturing complexities and uncertainties have impeded the transition from
material prototypes to commercial batteries, making prototype verification
critical to quality assessment. A fundamental challenge involves deciphering
intertwined chemical processes to characterize degradation patterns and their
quantitative relationship with battery performance. Here we show that a
physics-informed machine learning approach can quantify and visualize
temporally resolved losses concerning thermodynamics and kinetics only using
electric signals. Our method enables non-destructive degradation pattern
characterization, expediting temperature-adaptable predictions of entire
lifetime trajectories, rather than end-of-life points. The verification speed
is 25 times faster yet maintaining 95.1% accuracy across temperatures. Such
advances facilitate more sustainable management of defective prototypes before
massive production, establishing a 19.76 billion USD scrap material recycling
market by 2060 in China. By incorporating stepwise charge acceptance as a
measure of the initial manufacturing variability of normally identical
batteries, we can immediately identify long-term degradation variations. We
attribute the predictive power to interpreting machine learning insights using
material-agnostic featurization taxonomy for degradation pattern decoupling.
Our findings offer new possibilities for dynamic system analysis, such as
battery prototype degradation, demonstrating that complex pattern evolutions
can be accurately predicted in a non-destructive and data-driven fashion by
integrating physics-informed machine learning.

利用物理信息机器学习方法，我们展示了一种可以通过电信号量化和可视化描述热力学和动力学相关性的时态损失，实现非破坏性降解模式表征，加速整个使用寿命轨迹的温度自适应预测，速度提高了 25 倍，准确率保持在 95.1％，这种研究为电池原型的降解提供了新的可能性。