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Multiscale Modeling of Energy Materials
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Multiscale Modeling of Energy Materials

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Energy Materials".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 2692

Special Issue Editors

Dr. Hossein Mirhosseini

E-Mail Website
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

E-Mail Website
Guest Editor
Theoretical chemistry department, Universität Paderborn, Paderborn, Germany
Interests: energy materials; data-driven materials science; high-throughput calculations; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multiscale modeling
  • data-driven science
  • machine learning
  • high throughput
  • energy materials
  • energy storage
  • energy harvesting
  • green catalysis

Published Papers (1 paper)

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Research

13 pages, 6438 KiB  
Article
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 - 03 Nov 2021
Cited by 4 | Viewed by 1998
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 [...] Read more.
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 (LiC6−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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