Repository logo
Communities & Collections
All of DSpace
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Kekarjawlekar, P."

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Results Per Page
  • Sort Options
  • No Thumbnail Available
    Item
    Computational materials discovery and development for Li and non-Li advanced battery chemistries
    (International Association of Physical Chemists, 2023) Sharma, H.; Nazir, A.; Kasbe, A.; Kekarjawlekar, P.; Chatterjee, K.; Motevalian, S.; Claus, A.; Prakash, V.; Acharya, S.; Sahu, K.K.
    Since the discovery of batteries in the 1800s, their fascinating physical and chemical properties have led to much research on their synthesis and manufacturing. Though lithium-ion batteries have been crucial for civilization, they can still not meet all the growing demands for energy storage because of the geographical distribution of lithium resources and the intrinsic limitations in the cell energy density, performance, and reliability issues. As a result, non-Li-ion batteries are becoming increasingly popular alternatives. Designing novel materials with desired properties is crucial for a quicker transition to the green energy ecosystem. Na, K, Mg, Zn, Al ion, etc. batteries are considered the most alluring and promising. This article covers all these Li, non-Li, and metal-air cell chemistries. Recently, computational screening has proven to be an effective tool to accelerate the discovery of active materials for all these cell types. First-principles methods such as density functional theory, molecular dynamics, and Monte Carlo simulations have become established techniques for the preliminary, theoretical analysis of battery systems. These computational methods generate a wealth of data that might be immensely useful in the training and validating of artificial intelligence and machine learning techniques to reduce the time and capital expenditure needed for discovering advanced materials and final product development. This review aims to summarize the application of these techniques and the recent developments in computational methods to discover and develop advanced battery chemistries. © 2023 by the authors; licensee IAPC, Zagreb, Croatia.

Maintained by Central Library NITK | DSpace software copyright © 2002-2026 LYRASIS

  • Privacy policy
  • End User Agreement
  • Send Feedback
Repository logo COAR Notify