Computational materials discovery and development for Li and non-Li advanced battery chemistries: Review paper

Henu Sharma; Aqsa Nazir; Arvind Kasbe; Prathamesh  Kekarjawlekar; Kajari Chatterjee; Saeme Motevalian; Ana Claus; Viswesh Prakash; Sagnik Acharya; Kisor K. Sahu

doi:10.5599/jese.1713

Authors

Henu Sharma NetTantra Technologies India Pvt. Ltd, Bhubaneswar, India and School of Mineral, Metallurgical, and Materials Engineering, Indian Institute of Technology Bhubaneswar, India https://orcid.org/0000-0002-6928-3067
Aqsa Nazir Department of Mechanical and Materials Engineering, Florida International University, Florida, USA https://orcid.org/0000-0003-3075-2476
Arvind Kasbe Ecoworld Pharm, Jeollanam-do, South Korea https://orcid.org/0000-0002-5181-1204
Prathamesh Kekarjawlekar School of Mineral, Metallurgical, and Materials Engineering, Indian Institute of Technology Bhubaneswar, India https://orcid.org/0000-0001-8689-3396
Kajari Chatterjee School of Mineral, Metallurgical, and Materials Engineering, Indian Institute of Technology Bhubaneswar, India https://orcid.org/0000-0002-7333-8091
Saeme Motevalian Department of Mechanical and Materials Engineering, Florida International University, Florida, USA https://orcid.org/0009-0006-3464-8368
Ana Claus Department of Mechanical and Materials Engineering, Florida International University, Florida, USA https://orcid.org/0009-0004-2297-1681
Viswesh Prakash Department of Metallurgical and Materials Engineering, National Institute of Technology Karnataka, Surathkal, India https://orcid.org/0009-0003-2274-2507
Sagnik Acharya School of Mineral, Metallurgical, and Materials Engineering, Indian Institute of Technology Bhubaneswar, India https://orcid.org/0009-0007-2891-9050
Kisor K. Sahu School of Mineral, Metallurgical, and Materials Engineering, Indian Institute of Technology Bhubaneswar, India https://orcid.org/0000-0003-2488-0420

DOI:

https://doi.org/10.5599/jese.1713

Keywords:

DFT, machine learning, artificial intelligence, molecular dynamic simulation, Monte Carlo simulations, metal-air batteries

Abstract

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.

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Computational materials discovery and development for Li and non-Li advanced battery chemistries

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