Density Functional Theory: Dispersion Interactions & Biological Applications
Bibliographic record
Abstract
London or dispersion interactions are weak van der Waals (vdW) interactions. They are important in determining the structure and properties of many chemical and biochemical systems. In this thesis, an optimizer using the nonempirical generalized gradient approximation (GGA) functional PW86+PBE+XDM, to capture van der Waals interactions, is presented. The work in this thesis covers the assessment of a variety of basis sets for their ability to reproduce accurate GGA repulsive and binding energies. Selected basis sets were then used to compute binding energies of 65 vdW complexes at equilibrium. This functional was also tested for binding energies of two sets of vdW complexes at distorted geometries. The last part deals with forces to investigate their accuracy using PW86+PBE+XDM in order to build an optimizer for vdW complexes using a nonempirical DFT method. Eventually, after confirming a high reproducibility of the optimizer on the geometries and binding energies, it was used in two biologically relevant applications. This optimizer is a unique tool to compute deformation energies with a nonempirical DFT method. The second part of this thesis covers a biologically relevant application where a conventional DFT is used. This application is related to the carrier of the genetic codes in living cells, DNA. DNA undergoes harmful mutations under external perturbations such as applied external electric fields. In this study, DNA base pairs were first mimicked by a simpler model, namely, the formic acid dimer. The effect of applied external electric fields on the geometries of the formic acid dimer is studied. The effect of these applied fields on the potential energy surface, the barrier height and the frequency of the double proton transfer in the formic acid dimer are also investigated. The study was then repeated on DNA base pairs to study the effect of an external applied electric field on the tunneling corrected rate constants of the double proton transfer reactions in AT and GC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".