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。

基于物理信息机器学习的超早期电池样机验证的非破坏性降解模式解耦

Non-destructive Degradation Pattern Decoupling for Ultra-early Battery  Prototype Verification Using Physics-informed Machine Learning

Blind face restoration (BFR) is a highly challenging problem due to the
uncertainty of degradation patterns. Current methods have low generalization
across photorealistic and heterogeneous domains. In this paper, we propose a
Diffusion-Information-Diffusion (DID) framework to tackle diffusion manifold
hallucination correction (DiffMAC), which achieves high-generalization face
restoration in diverse degraded scenes and heterogeneous domains. Specifically,
the first diffusion stage aligns the restored face with spatial feature
embedding of the low-quality face based on AdaIN, which synthesizes
degradation-removal results but with uncontrollable artifacts for some hard
cases. Based on Stage I, Stage II considers information compression using
manifold information bottleneck (MIB) and finetunes the first diffusion model
to improve facial fidelity. DiffMAC effectively fights against blind
degradation patterns and synthesizes high-quality faces with attribute and
identity consistencies. Experimental results demonstrate the superiority of
DiffMAC over state-of-the-art methods, with a high degree of generalization in
real-world and heterogeneous settings. The source code and models will be
public.

提出了一种 Diffusion-Information-Diffusion（DID）框架用于解决不同场景和异构领域中的难以预测的盲目面部修复问题，通过进行扩散流形幻觉修正（DiffMAC），实现了多样化退化场景中高通用性的面部恢复，该方法通过 Stage I 对低质量人脸的空间特征嵌入进行修复对齐，并在 Stage II 中通过流形信息瓶颈（MIB）进行信息压缩和优化，有效抵御盲目退化模式，综合合成具有一致性的高质量人脸。实验结果表明 DiffMAC 相对于最先进的方法具有优越性，并在真实世界和异构环境中具有较高的泛化能力。