Machine learning-inspired battery material innovation

Abstract

Machine learning (ML) techniques have been a powerful tool responsible for many new discoveries in materials science in recent years. In the field of energy storage materials, particularly battery materials,...

Radiology Decision Support System for Selecting Appropriate CT Imaging Titles Using Machine Learning Techniques Based on Electronic Medical Records

“…It has been used widely to evaluate classifier performance when encountering a rare class [52]. Although the accuracy values were slightly larger than the F1-scores in most models, we considered the F1-score to be a more appropriate reflection of acceptable model performance because it is a ''single-number evaluation metric'' [53], which is a harmonic (i.e., weighted) mean of precision and recall used for model comparison [22]. Whereas the regular mean treats all values equally, the weighted mean assigns more weight to low frequencies.…”

Advancements in cathode materials for potassium-ion batteries: current landscape, obstacles, and prospects

“…It is noteworthy that reversible anion-redox chemistry has been observed in certain layered oxide cathode materials, such as K 0.4 Fe 0.5 Mn 0.5 O 2 238 and K 0.78 Fe 1.60 S 2 . 250 These approaches, including high-throughput materials screening, [274][275][276] hold promise in addressing the potassium deficiency challenge and unlocking the full potential of layered oxide cathode materials for high-performance KIBs. Further research in this direction is crucial for advancing the field of potassium-ion battery technology and enhancing its practical applications in energy storage.…”

“…These approaches, including high-throughput materials screening, 274–276 hold promise in addressing the potassium deficiency challenge and unlocking the full potential of layered oxide cathode materials for high-performance KIBs. Further research in this direction is crucial for advancing the field of potassium-ion battery technology and enhancing its practical applications in energy storage.…”

Machine Learning Assisted Screening of MXene with Superior Anchoring Effect in Al–S Batteries

“…In this regard, machine learning (ML) empowers us to discover novel patterns, make insightful predictions, and accelerate the discovery of materials with exceptional accuracies. − Kim and co-workers have resolved the capacity fading issues of unstable electrode materials by utilizing machine learning potential . Significant progress has been reported for the inverse design of materials, direct property prediction and utilization of ML potential with the advance ML techniques . With each iteration, our data-driven models become more refined, enhancing our ability to design MXenes tailored for specific applications.…”

“…45 Significant progress has been reported for the inverse design of materials, direct property prediction and utilization of ML potential with the advance ML techniques. 46 With each iteration, our data-driven models become more refined, enhancing our ability to design MXenes tailored for specific applications. This synergistic combination of ML and materials design revolutionizes the process, unlocking unprecedented opportunities for the development of application specific high-performance MXene-based systems.…”