Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
5.3
Journals
Nanomaterials
Special Issues
Theoretical Chemistry and Computational Simulations in Nanomaterials
share announcement

Theoretical Chemistry and Computational Simulations in Nanomaterials

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Theory and Simulation of Nanostructures".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 2321

Special Issue Editors

Dr. Dashuai Wang

E-Mail Website
Guest Editor
1. Institute of Zhejiang University-Quzhou, Quzhou 324000, China
2. College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
Interests: first-principles calculations; computational chemistry; electrocatalytic; atomic catalyst; energy storage and conversion
Special Issues, Collections and Topics in MDPI journals
Dr. Xianyun Peng

E-Mail Website
Guest Editor
1. Institute of Zhejiang University-Quzhou, Quzhou 324000, China
2. College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
Interests: first-principles calculations; electrocatalytic; electrosynthesis; atomic catalyst; small molecule value-added conversion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the significance of theoretical chemistry and computational simulations in the field of nanomaterials. With the rapid advancement of nanoscience and nanotechnology, researchers increasingly rely on theoretical and computational approaches for the design, prediction of properties, and optimization of nanomaterials. Theoretical chemistry and computational simulations play a pivotal role in elucidating fundamental principles, in explaining experimental phenomena, and in guiding the synthesis and applications of nanomaterials. This Special Issue provides a platform for researchers to exchange ideas, showcase cutting-edge research findings, and discuss methodological developments.

This Special Issue aligns closely with the scope of the journal Nanomaterials, which focuses on the publication of research papers addressing both scientific and applied aspects of nanomaterials. It offers an excellent opportunity to present the latest advancements in theoretical chemistry and computational simulations within the field of nanomaterials. By bringing together researchers from various disciplines, this Special Issue aims to facilitate interdisciplinary collaborations and contributions to the advancement of knowledge in this area.

We welcome original research articles and reviews that cover a wide range of topics, including (but not limited to):

  1. The theoretical design and computational prediction of nanomaterials;
  2. The simulation and interpretation of nanomaterial properties and responses;
  3. Theoretical investigations into nanomaterial interfaces and interfacial interactions;
  4. The quantum effects and electronic structure calculations of nanomaterials;
  5. The simulation of mechanical properties and deformation behavior of nanomaterials;
  6. The modeling of thermal transport and the thermodynamics of nanomaterials;
  7. The computational modeling of nanocatalysts and catalytic reactions;
  8. Computational investigations into nanomaterials for energy storage applications;
  9. Machine learning approaches for nanomaterial discovery and design.

Dr. Dashuai Wang
Dr. Xianyun Peng
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. Nanomaterials 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 2900 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

  • theoretical chemistry
  • computational simulations
  • density functional theory
  • quantum effects
  • electronic structure calculations
  • molecular dynamics
  • multiscale modeling
  • nanocatalysis
  • energy storage
  • machine learning

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

11 pages, 3579 KiB  
Article
Rational Design of Non-Noble Metal Single-Atom Catalysts in Lithium–Sulfur Batteries through First Principles Calculations
by Yang Li, Yao Liu, Jinhui Zhang, Dashuai Wang and Jing Xu
Nanomaterials 2024, 14(8), 692; https://doi.org/10.3390/nano14080692 - 17 Apr 2024
Viewed by 297
Abstract
Lithium–sulfur (Li–S) batteries with a high theoretical energy density of 2600 Wh·kg−1 are hindered by challenges such as low S conductivity, the polysulfide shuttle effect, low S reduction conversion rate, and sluggish Li2S oxidation kinetics. Herein, single-atom non-noble metal catalysts [...] Read more.
Lithium–sulfur (Li–S) batteries with a high theoretical energy density of 2600 Wh·kg−1 are hindered by challenges such as low S conductivity, the polysulfide shuttle effect, low S reduction conversion rate, and sluggish Li2S oxidation kinetics. Herein, single-atom non-noble metal catalysts (SACs) loaded on two-dimensional (2D) vanadium disulfide (VS2) as the potential host materials for the cathode in Li–S batteries were investigated systematically by using first-principles calculations. Based on the comparisons of structural stability, the ability to immobilize sulfur, electrochemical reactivity, and the kinetics of Li2S oxidation decomposition between these non-noble metal catalysts and noble metal candidates, Nb@VS2 and Ta@VS2 were identified as the potential candidates of SACs with the decomposition energy barriers for Li2S of 0.395 eV (Nb@VS2) and of 0.162 eV (Ta@VS2), respectively. This study also identified an exothermic reaction for Nb@VS2 and the Gibbs free energy of 0.218 eV for Ta@VS2. Furthermore, the adsorption and catalytic mechanisms of the VS2-based SACs in the reactions were elucidated, presenting a universal case demonstrating the use of unconventional graphene-based SACs in Li–S batteries. This study presents a universal surface regulation strategy for transition metal dichalcogenides to enhance their performance as host materials in Li–S batteries. Full article
(This article belongs to the Special Issue Theoretical Chemistry and Computational Simulations in Nanomaterials)
►▼ Show Figures

Graphical abstract

10 pages, 1781 KiB  
Article
Feature-Assisted Machine Learning for Predicting Band Gaps of Binary Semiconductors
by Sitong Huo, Shuqing Zhang, Qilin Wu and Xinping Zhang
Nanomaterials 2024, 14(5), 445; https://doi.org/10.3390/nano14050445 - 28 Feb 2024
Viewed by 816
Abstract
The band gap is a key parameter in semiconductor materials that is essential for advancing optoelectronic device development. Accurately predicting band gaps of materials at low cost is a significant challenge in materials science. Although many machine learning (ML) models for band gap [...] Read more.
The band gap is a key parameter in semiconductor materials that is essential for advancing optoelectronic device development. Accurately predicting band gaps of materials at low cost is a significant challenge in materials science. Although many machine learning (ML) models for band gap prediction already exist, they often suffer from low interpretability and lack theoretical support from a physical perspective. In this study, we address these challenges by using a combination of traditional ML algorithms and the ‘white-box’ sure independence screening and sparsifying operator (SISSO) approach. Specifically, we enhance the interpretability and accuracy of band gap predictions for binary semiconductors by integrating the importance rankings of support vector regression (SVR), random forests (RF), and gradient boosting decision trees (GBDT) with SISSO models. Our model uses only the intrinsic features of the constituent elements and their band gaps calculated using the Perdew–Burke–Ernzerhof method, significantly reducing computational demands. We have applied our model to predict the band gaps of 1208 theoretically stable binary compounds. Importantly, the model highlights the critical role of electronegativity in determining material band gaps. This insight not only enriches our understanding of the physical principles underlying band gap prediction but also underscores the potential of our approach in guiding the synthesis of new and valuable semiconductor materials. Full article
(This article belongs to the Special Issue Theoretical Chemistry and Computational Simulations in Nanomaterials)
►▼ Show Figures

Figure 1

20 pages, 9234 KiB  
Article
Dynamic Clustering and Scaling Behavior of Active Particles under Confinement
by Matthew Becton, Jixin Hou, Yiping Zhao and Xianqiao Wang
Nanomaterials 2024, 14(2), 144; https://doi.org/10.3390/nano14020144 - 09 Jan 2024
Viewed by 899
Abstract
A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force, is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws are derived with [...] Read more.
A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force, is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws are derived with power relationships for the dynamic clustering time as a function of both particle density and active driving force. Notably, the average number of clusters N¯ assembled from active particles in the simulation system exhibits a scaling relationship with clustering time t described by N¯∝t−m. Simultaneously, the scaling behavior of the average cluster size S¯ is characterized by S¯∝tm. Our findings reveal the presence of up to four distinct dynamic regions concerning clustering over time, with transitions contingent upon the particle density within the system. Furthermore, as the active driving force increases, the aggregation behavior also accelerates, while an increase in density of active particles induces alterations in the dynamic procession of the system. Full article
(This article belongs to the Special Issue Theoretical Chemistry and Computational Simulations in Nanomaterials)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop