Artificial intelligence applied to battery research: hype or reality?
T Lombardo, M Duquesnoy, H El-Bouysidy… - Chemical …, 2021 - ACS Publications
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …
Classical dynamical density functional theory: from fundamentals to applications
Classical dynamical density functional theory (DDFT) is one of the cornerstones of modern
statistical mechanics. It is an extension of the highly successful method of classical density …
statistical mechanics. It is an extension of the highly successful method of classical density …
Perspective—combining physics and machine learning to predict battery lifetime
Forecasting the health of a battery is a modeling effort that is critical to driving improvements
in and adoption of electric vehicles. Purely physics-based models and purely data-driven …
in and adoption of electric vehicles. Purely physics-based models and purely data-driven …
The application of data-driven methods and physics-based learning for improving battery safety
Enabling accurate prediction of battery failure will lead to safer battery systems, as well as
accelerating cell design and manufacturing processes for increased consistency and …
accelerating cell design and manufacturing processes for increased consistency and …
Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel
Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to
quantify, yet are essential in engineering many chemical systems, such as batteries and …
quantify, yet are essential in engineering many chemical systems, such as batteries and …
A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches
One of the obstacles hindering the scaling-up of the initial successes of machine learning in
practical engineering applications is the dependence of the accuracy on the size and quality …
practical engineering applications is the dependence of the accuracy on the size and quality …
[HTML][HTML] Review of “grey box” lifetime modeling for lithium-ion battery: Combining physics and data-driven methods
Lithium-ion batteries are a popular choice for a wide range of energy storage system
applications. The current motivation to improve the robustness of lithium-ion battery …
applications. The current motivation to improve the robustness of lithium-ion battery …
Guiding the design of heterogeneous electrode microstructures for Li‐ion batteries: microscopic imaging, predictive modeling, and machine learning
Electrochemical and mechanical properties of lithium‐ion battery materials are heavily
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
Non-invasive inference of thrombus material properties with physics-informed neural networks
We employ physics-informed neural networks (PINNs) to infer properties of biological
materials using synthetic data. In particular, we successfully apply PINNs to extract the …
materials using synthetic data. In particular, we successfully apply PINNs to extract the …
Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space
The phase-field method is a popular modeling technique used to describe the dynamics of
microstructures and their physical properties at the mesoscale. However, because in these …
microstructures and their physical properties at the mesoscale. However, because in these …