From Low-Dimensional Molecular Dynamics to High-Order Machine Learning: Advances in Computational Simulations in Catalysts
A special issue of Catalysts (ISSN 2073-4344). This special issue belongs to the section "Computational Catalysis".
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 381
Special Issue Editors
Interests: compute-assisted thermophysical property calculations of low-dimensional structures using MD/DFT; AI-driven MD simulation; structure discovery; accelerated computing
Interests: nanoscale heat conduction; micro/nanoscale flow and heat transfer; thermal management; first-principles calculations; molecular dynamics simulation
Special Issues, Collections and Topics in MDPI journals
Interests: quantum dot synthesis; quantum dot optoelectronics (PV, LED, photodetector, display and image sensors); electron microscopy (TEM) and dynamic charge transfer analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Given the importance of catalysts in the energy industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. We welcome recent research on computational studies including energy conversion, energy transfer, material growth, dehydrogenation, hydrogenation, oxidation reactions, and recombination of carbon materials that can guide catalyst calculations.
Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification are also welcome in this Special Issue. This Special Issue aims to collect a list of articles and reviews that provide state-of-the-art insights into recent developments in computer-aided studies in catalysts and energy.
Dr. Jingchao Zhang
Prof. Dr. Xinyu Wang
Prof. Dr. Bo Hou
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. 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 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
- Computational study
- Machine learning
- Artificial neural network
- Methodology development
- Catalytic reaction
- Catalytic mechanistic
- Optimal design of catalysts
- Transition state search
- Energy conversion/transfer
- Micro/nanoscale devices
