Abstract
Hydrogen and electricity are the leading energy sources for most applications in the near future. With the rapid development of computers and computational chemistry methods, the research in hydrogen production, infrastructure materials, storage, and utilization more than ever benefits from close interactions with computational chemistry. The present review aims at presenting the modern state of hydrogen energy chemical research in all processes of hydrogen economy. Directions of further research efforts are given in the context of chemometrics and computational chemistry advancements. Problems of computational chemistry that hinder rapid progress are given and their possible solutions are outlined. It is expected that new artificial neural network (ANN) algorithms will play a decisive role in the creation of dependable computational chemistry methods and their application for hydrogen infrastructure, production, storage, and consumption.
| Original language | English |
|---|---|
| Pages (from-to) | 14978-14999 |
| Number of pages | 22 |
| Journal | International Journal of Hydrogen Energy |
| Volume | 48 |
| Issue number | 40 |
| DOIs | |
| State | Published - 8 May 2023 |
Keywords
- Chemometrics
- DFT
- Data science
- Machine learning
- Review
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