Special Issue "10th Anniversary of Catalysts: Achievements in Computational Catalysis Techniques and Applications"
Deadline for manuscript submissions: closed (31 December 2021).
Interests: computational catalysis; DFT calculations; kinetic Monte Carlo simulations; electrocatalysis; adsorption; porous materials; interfacial catalysis; nanoparticle synthesis; polymeric membranes; separations
Special Issues, Collections and Topics in MDPI journals
Interests: computational catalysis; electronic structure calculations; molecular simulations; liquid crystals; interfacial phenomena; materials for energy applications
Interests: computational catalysis; electrocatalysis; biooils upgrading reactions; CO2 reduction reaction; oxygen reduction reaction; DFT calculations
Interests: computational chemistry; density functional theory; molecular dynamics simulation; machine learning; heterogeneous catalysis; energy catalytic materials; single-atom catalysis
In 2021, Catalysts will reach a significant milestone in its history by welcoming its tenth anniversary. In order to celebrate this special occasion, we will be launching a Special Issue in the Computational Catalysis subsection entitled “10th Anniversary of Catalysts: Achievements in Computational Catalysis Techniques and Applications.” We will be editing a Special Issue of comprehensive reviews and particularly impactful original articles. Computational catalysis has emerged as one of the fastest growing research fields in the last decade, and it now represents a critical tool for the analysis of chemical mechanisms and active sites. As the field of computational catalysis continues to expand, the gap between models and reality is beginning to narrow. We are particularly interested in articles that investigate the secondary effects influencing catalysis and reaction mechanisms. This includes the role of structural defects, the solvation environment or neighbor-neighbor effects, deactivation events, and work that incorporates system features encountered at finite temperatures. Furthermore, we are interested in new techniques and applications that enable extended time scale analyses, as well as high throughput screening techniques that involve machine learning or descriptor-based protocols.
We would like to thank all our Editorial Board Members, Editors, Reviewers, and Authors for their great contributions and continuous support over the last decade. Please help us to celebrate our 10th Anniversary and participate by submitting your work to this Special Issue.
Prof. Dr. C. Heath Turner
Prof. Dr. Tibor Szilvási
Prof. Dr. Wei An
Prof. Dr. Yaqiong Su
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 papers will be 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. Catalysts is an international peer-reviewed open access monthly 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 2200 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.
- Ab initio
- Density-functional theory
- Reaction mechanism
- Kinetic Monte Carlo
- Machine learning