Experimental methods in chemical engineering: Density functional theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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 solid‐state 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 15 000. A bibliometric analysis of the top 10 000 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it