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Multiscale Modeling of Energy Materials

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Dr. Hossein Mirhosseini

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Guest Editor

Theoretical Chemistry Department, Universität Paderborn, Paderborn, Germany
Interests: energy materials; data-driven materials science; high-throughput calculations; machine learning

Prof. Dr. Thomas D. Kühne

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Guest Editor

Theoretical chemistry department, Universität Paderborn, Paderborn, Germany
Interests: energy materials; data-driven materials science; high-throughput calculations; machine learning
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Dear Colleagues,

The transition from fossil energy sources to renewable and clean energy sources requires a new generation of advanced materials for low-carbon energy technologies. In addition, complex processes governing the behavior of devices for energy harvesting, storage, and conversion need to be fundamentally understood to enable device performance enhancement.

Numerical modeling and computational simulations are inevitable parts of modern materials science. In conjunction with experiments, multiscale modeling techniques (from quantum mechanics to device modeling) are needed to gain insight into phenomena that govern material behavior. In addition to conventional computational modeling, state-of-the-art techniques such as data-driven science and artificial intelligence are likely to advance materials research. With ever-increasing computer power and the rapid development of databases, data-driven science has enabled the rational design and development of novel materials. Additionally, machine-learning-aided computational techniques are becoming invaluable and powerful tools in the study and design of matter.

This Special Issue of Materials will focus on the multiscale modeling and simulation techniques from diverse scientific disciplines that enable the investigation of energy materials. It is our pleasure to invite you to submit a manuscript (full papers, communications, and reviews) for this Special Issue of Materials.

Dr. Hossein Mirhosseini
Prof. Dr. Thomas D. Kühne
Guest Editors

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Research

13 pages, 6438 KiB

Open AccessArticle

Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

by Simon Anniés, Chiara Panosetti, Maria Voronenko, Dario Mauth, Christiane Rahe and Christoph Scheurer

Materials 2021, 14(21), 6633; https://doi.org/10.3390/ma14216633 - 3 Nov 2021

Cited by 6 | Viewed by 2614

Abstract

Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC_6−x;

x > 0

) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations. Full article

(This article belongs to the Special Issue Multiscale Modeling of Energy Materials)

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