Computational Chemistry in Metallurgy, Materials and Energy
Computational chemistry has progressed significantly in recent decades due to the rapid advancement of supercomputers and algorithms. Various ab initio and semi-empirical methods combined with the most advanced machine learning and enhanced sampling techniques are now freely available in many open-sourced packages. Even though these methods were originally used in fundamental physics and chemistry, applications in relatively traditional and application-oriented research areas, such as metallurgy, materials and energy, have emerged rapidly in recent years. Atomistic simulation techniques in computational chemistry have so far been instrumental in an atomistic-scale understanding of complex mechanisms and structures in severe conditions with high temperatures or pressures, which are almost inaccessible by experimentation but are essential for the optimization of processes and the tuning of product properties. The present Topic is aimed at presenting the most advanced computational chemistry methods to understand the evolution of matter structures and properties, as well as reaction mechanisms in the processes related to metallurgy, materials and energy. While the research objectives are very broad, methods combined with state-of-the-art artificial intelligence and enhanced sampling techniques to obtain the potential energy landscape, along with structural evolution, are more than welcome. We invite authors to contribute original research articles and review articles covering the current progress in these areas. Potential topics include, but are not limited to:
- Computational chemistry in metallurgy, including the study of any raw materials or reactions in both ferrous and non-ferrous metallurgical process
- Computational chemistry in materials, especially for the study of novel carbonaceous materials and two-dimensional materials with new structures or advanced properties
- Computational chemistry in energy, especially the study of the transformation mechanisms of various kinds of fossil and non-fossil fuels including coal, biomass, etc
- Computational chemistry in any other area with an aim of understanding structures or mechanisms at an atomistic scale.
Prof. Dr. Kejiang Li
Prof. Dr. Guangyue Li
Dr. Qifan Zhong
|First Decision (median)
Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.
MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:
- Immediately share your ideas ahead of publication and establish your research priority;
- Protect your idea from being stolen with this time-stamped preprint article;
- Enhance the exposure and impact of your research;
- Receive feedback from your peers in advance;
- Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.