Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

Joshi, Ritesh; Eickholt, Jesse; Li, Liling; Fornari, Marco; Barone, Verónica; Peralta, Juan E.

doi:10.1021/acsami.9b04933

Cited by 132 publications

(114 citation statements)

References 66 publications

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“…Other important properties, such as voltages, elastic moduli, etc., are easier to obtain reliably via high‐throughput computations. To date, there are relatively few ML works that target these and other properties for the purposes of screening, which presents major opportunities for further exploration. More promisingly, ML‐IAPs are emerging as a powerful new tool that enable long‐time scale simulations of large systems at near‐DFT accuracy, providing critical atomistic scale insights into the phase transformation pathways and diffusion processes in battery materials.…”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

395

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

395

281

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“…Machine learning can be used in a similar way for experimental design and to shortcut costly experiments. Further evidence of the potential for machine learning to shortcut simulations comes from studies regarding the mechanical properties of solid electrolytes 82 and voltage 83 .…”

Section: Future Outlook and Opportunitiesmentioning

confidence: 99%

“…The combined database and machine learning approach have been applied to design and predict the material properties of electrodes such as voltage, crystallinity and chemical stability, from atomic scale to mesoscale 83,[94][95][96][97][98][99] . In addition, such an approach has been applied to design new liquid electrolytes and additives [100][101][102][103][104][105] , and solid-state electrolytes with fast Li-ion transport [106][107][108] and mechanical 82 properties.…”

Section: Future Outlook and Opportunitiesmentioning

confidence: 99%

Predicting the state of charge and health of batteries using data-driven machine learning

et al. 2020

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W ith rising concerns about global warming, electrification of transport has recently emerged as an important vision in many countries. The successful development of electric vehicles (EVs) depends highly on the cycling performance, cost and safety of the batteries. Rechargeable lithium-ion (Li-ion) batteries are currently the best choice for EVs due to their reasonable energy density and cycle life 1 . Further research and development on Li-ion batteries will lead to even higher energy density and more complicated battery dynamics, where the efficiency and safety of such batteries will become a concern. An advanced battery management system (BMS) that can monitor and optimize battery behaviour and safety is thus essential for the entire electrification system 2 .Today, one of the major barriers to widespread adoption of EVs is range anxiety. The ability of a BMS to accurately determine the state of charge (SOC) and state of health (SOH) of batteries, and hence the estimated driving range, will alleviate this problem. In addition, reliable prediction of remaining useful life (RUL) will allow batteries to be used to their fullest potential and maximum life expectancy before replacement or disposal. Knowledge of the RUL of spent batteries will also enable their redeployment in less demanding, second-life applications such as stationary grid storage. If we are able to sort manufactured cells based on their expected lifetime using early-cycle data, we can further accelerate the testing, validation and development process of new batteries. In summary, accurate prediction of the current and future state of batteries will open up vast opportunities in battery manufacturing, usage and optimization 3,4 . SOC and SOH are the two most important parameters in battery management and are generally defined as:where C curr is the capacity of the battery in its current state, C full is the capacity of the battery in its fully charged state, C nom is the nominal capacity of the brand-new battery 2 . In essence, SOC denotes the capacity of the battery in its current state compared to the capacity in its fully charged state (equivalent of a fuel gauge), while SOH describes the capacity of the battery in its fully charged state compared to the nominal capacity when brand new. By convention, SOC is 100% when the battery is fully charged and 0% when it is empty, while SOH is 100% at the time of manufacture and reaches 80% at end of life (EOL). In the battery manufacturing industry, EOL is often defined as the point at which the actual capacity at full charge drops to 80% of its nominal value 2 . The remaining number of charge/discharge cycles until the battery reaches EOL is the RUL of the battery. Current BMSs can determine the SOC of Li-ion batteries within 0.6% to 6.5% 5 , but are unable to predict the SOH and RUL of batteries accurately 6 .The traditional methods for SOC estimation include ampere hour counting estimation, open-circuit voltage-based estimation, impedance-based estimation, model-based estimation, fuzzy logic, and Kal...

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“…The ML results indicate that the topology of Li layers and relative disposition of Li and dopants in NCA are the most important descriptors in energy balance estimations. Moreover, ML methods have been also applied to predict the potential of electrode materials for other metal‐ion batteries …”

Section: Achievements Of ML In Energy Storage and Conversion Materialsmentioning

confidence: 99%

Machine learning: Accelerating materials development for energy storage and conversion

Chen

Zhou

2020

InfoMat

246

171

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With the development of modern society, the requirement for energy has become increasingly important on a global scale. Therefore, the exploration of novel materials for renewable energy technologies is urgently needed. Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost. Nowadays, machine learning (ML) is rising as a new research paradigm to revolutionize materials discovery.In this review, we briefly introduce the basic procedure of ML and common algorithms in materials science, and particularly focus on latest progress in applying ML to property prediction and materials development for energyrelated fields, including catalysis, batteries, solar cells, and gas capture. Moreover, contributions of ML to experiments are involved as well. We highly expect that this review could lead the way forward in the future development of ML in materials science. K E Y W O R D Sbig data, energy storage and conversion, machine learning, property prediction

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Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries

Cited by 132 publications

References 66 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Predicting the state of charge and health of batteries using data-driven machine learning

Machine learning: Accelerating materials development for energy storage and conversion

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