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From Atoms to Applications: Computational Design, Prediction, and Simulation of Materials

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 531

Special Issue Editors


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Guest Editor
Department of Chemistry and Biochemistry, Utah State University, Logan, UT, USA
Interests: quantum chemistry; h-bonds; noncovalent bonds; tetrel bonds; pnicogen bonds; chalcogen bonds; halogen bonds; structure and function of biomolecules like proteins
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Guest Editor Assistant
Department of Chemistry, University of California, Berkeley, CA 94720, USA
Interests: physical chemistry; quantum chemistry; quantum computing; materials science; chemical bonding

Special Issue Information

Dear Colleagues,

Every technological advancement is directly linked to a specific set of materials. Currently, our society faces numerous pressing challenges that require innovative material solutions, such as the development of efficient green energy technologies, the energy storage of environmentally benign fuels, carbon capture, and others. These demands require the discovery of new materials with a very specific narrow spectrum of properties for each application. In addition, computational materials design already plays a central role in the rapidly evolving field of materials science. As the experimental discovery of new materials is an inherently costly and time-consuming process, computationally guided synthesis will accelerate innovation. With the development of theoretical methods for electronic-structure simulations, molecular dynamics, Monte Carlo simulations, thermodynamic sampling techniques, crystal structure prediction, global minima searches of high-dimensional potential energy surfaces, and others, the importance and applicability of computational materials design are expected to grow substantially.

This Special Issue intends to provide a comprehensive overview of the dynamic and rapidly evolving field of computational materials science. We aim to showcase the latest advancements and applications in the computational design, prediction, and simulation of materials. We invite researchers to submit their cutting-edge work that highlights the critical importance of computational methods in materials science.

Prof. Dr. Steve Scheiner
Dr. Nikolay V. Tkachenko
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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

  • molecular modeling
  • machine learning
  • materials science
  • computational techniques
  • theoretical and computational chemistry
  • molecular dynamics

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Published Papers (1 paper)

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Research

16 pages, 2877 KiB  
Article
From Aromatic Motifs to Cluster-Assembled Materials: Silicon–Lithium Nanoclusters for Hydrogen Storage Applications
by Williams García-Argote, Erika Medel, Diego Inostroza, Alejandro Vásquez-Espinal, José Solar-Encinas, Luis Leyva-Parra, Lina María Ruiz, Osvaldo Yañez and William Tiznado
Molecules 2025, 30(10), 2163; https://doi.org/10.3390/molecules30102163 - 14 May 2025
Viewed by 182
Abstract
Silicon–lithium clusters are promising candidates for hydrogen storage due to their lightweight composition, high gravimetric capacities, and favorable non-covalent binding characteristics. In this study, we employ density functional theory (DFT), global optimization (AUTOMATON and Kick–MEP), and Born–Oppenheimer molecular dynamics (BOMD) simulations to evaluate [...] Read more.
Silicon–lithium clusters are promising candidates for hydrogen storage due to their lightweight composition, high gravimetric capacities, and favorable non-covalent binding characteristics. In this study, we employ density functional theory (DFT), global optimization (AUTOMATON and Kick–MEP), and Born–Oppenheimer molecular dynamics (BOMD) simulations to evaluate the structural stability and hydrogen storage performance of key Li–Si systems. The exploration of their potential energy surface (PES) reveals that the true global minima of Li6Si6 and Li10Si10 differ markedly from those of the earlier Si–Li structures proposed as structural analogs of aromatic hydrocarbons such as benzene and naphthalene. Instead, these clusters adopt compact geometries composed of one or two Si4 (Td) units and a Si2 dimer, all stabilized by surrounding Li atoms. Motivated by the recurrence of the Si4Td motif, we explore oligomers of Li4Si4, which can be viewed as electronically transmuted analogues of P4, confirming the additive H2 uptake across dimer, trimer, and tetramer assemblies. Within the series of Si–Li clusters evaluated, the Li12Si5 sandwich complex, featuring a σ-aromatic Si510− ring encapsulated by two Li65+ moieties, achieves the highest hydrogen capacity, adsorbing 34 H2 molecules with a gravimetric density of 23.45 wt%. Its enhanced performance arises from the high density of accessible Li+ adsorption sites and the electronic stabilization afforded by delocalized σ-bonding. BOMD simulations at 300 and 400 K confirm their dynamic stability and reversible storage behavior, while analysis of the interaction regions confirms that hydrogen adsorption proceeds via weak, dispersion-driven physisorption. These findings clarify the structure–property relationships in Si–Li clusters and provide a basis for designing modular, lightweight, and thermally stable hydrogen storage materials. Full article
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