Bibliographic record
Abstract
The current research concerns the efficient, i.e., low cost computational modelling of interface properties through the development of advanced methodologies to model interface properties in complex systems, for instance, the interaction energy between a nanomaterial and a biomolecule in a solvent.The design of materials in catalysis, energy conversion, nanomedicine and nanosafety is of great importance [1] and has significantly improved by computational materials modelling.In the present study, quantum chemical methods are used to describe electronic structure properties of material surfaces, while molecular dynamics simulations are used to compute interaction energies between the materials and chemical or biochemical compounds.The materials of interest include metals, semiconductors, carbon nanotubes, polymers, amorphous carbon and graphene.Intermolecular interactions between nanomaterials and chemical or biomolecular compounds in water are studied through molecular dynamics simulations of potentials of mean forces [2, 3], e.g., the interaction energy of a protein adsorbed on a metal surface in water is computed on the atomistic and meso-scale, where molecular and coarse grained force fields are utilised in the simulations, while water interface properties, such as the wettability of material surfaces, are obtained through molecular dynamics simulations of contact angles and immersion enthalpies [4].Moreover, the free energy of solvation (water) including the vibrational entropy of the materials consists of a novel descriptor [5], and is examined through molecular dynamics simulations on the semi-empirical tight binding density functional theory level [6].The interaction energies of aggregated nanoparticles in water [3,7] and the dissolution of ions from a metal (oxide) surface are significant interface descriptors in the quest for safe materials.Latter descriptors are obtained through calculations of the free energy of the material with an atomic vacancy on the surface layer [5].
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".