Density Functional Theory (DFT) application on Single Atom Catalysts (SACs) for the electrochemical reduction of carbon dioxide
Bibliographic record
Abstract
Density functional theory (DFT) is a quantum mechanics method that has been used by physicists, chemists and material scientists since the 1970s.It allows for the compution of important properties of materials and extract valuable information of chemical reactions.Chemical engineers and researchers use this method to enrich and justify their experimental findings, as well as discover new materials for various trending applications such as batteries, catalysis, photovoltaics and nanotechnology.One such important application is the electrochemical conversion of carbon dioxide intro high-valued products, such as methane and methanol.However, an adequate catalyst is needed to make this technology industrially feasable.This catalyst must selectively convert CO2 into one product, while minimizing the Abstract Density functional theory (DFT) computations apply to physics, chemistry, material science, and engineering.In chemical engineering, DFT identifies material structure and properties, and mechanisms for phenomena such as chemical reaction and phase transformation that are otherwise impossible to measure experimentally.Even though its practical application dates back only a decade or two, it is already a standard tool for materials modelling.Many textbooks and articles describe the theoretical basis of DFT, but it remains difficult for researchers to autonomously learn the steps to accurately calculate system properties.Here, we first explain the foundations of DFT in a way accessible to chemical engineers with little background in quantum mechanics or solidstate physics.Then, we introduce the basics of the computations and, for most of the rest of the article, we show how to derive physical characteristics of interest to chemical engineers: elastic, thermodynamic, and surface properties, electronic structure, and surface and chemical reaction energy.Finally, we highlight some limitations of DFT; since these calculations are approximations to the Schrödinger equation, their accuracy relies on choosing adequate exchange-correlation functions and basis sets.Since 1991, the number of articles WoS has indexed related to DFT has increased quadratically with respect to time and now numbers 15000.A bibliometric analysis of the top 10000 cited articles in 2018 and 2019 classifies them into four clusters: adsorption, graphene, and nanoparticles; ab initio molecular dynamics and crystal structure; electronic structure and optical properties; and total energy calculations and wave basis sets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".