Accurate Prediction of Mechanical Property of Organic Crystals Using Molecular Dynamics-Based Nanoindentation Simulations
Bibliographic record
Abstract
Developing reliable methods to predict the mechanical response of organic crystals is essential due to their growing recognition as soft, compliant, lightweight, and ultrafast smart dynamic materials that have applications ranging from pharmaceuticals to biomaterials. Traditional experimental methods, such as nanoindentation, come with practical convenience such as simplicity and efficacy; however, they are restrictive with regard to the surface quality and are subject to other experimental constraints such as the availability of suitable samples and accessibility of as-grown crystal faces. Computational approaches, including quantum mechanics and classical molecular dynamics (MD), offer an alternative approach that is independent of the sample, yet they are known to vary greatly in their predictive accuracy. To account for this shortfall of predictive tools, in this study, we systematically benchmark three major computational methods, including density functional theory (DFT), MD-based approaches based on deformation, and direct nanoindentation simulations, against experimental Young's moduli. Our results show that the MD-simulated process of indentation conforms most favorably with the experiments and has the lowest mean absolute error. The intermolecular interactions, crystal size, indenter spacing, and unloading rates are identified as the key factors that determine the stiffness, as given by the modulus. Our findings underscore the importance of incorporating both thermal effects and experimental conditions into computational models for improved predictive accuracy and highlight the potential of nanoindentation simulations as a reliable tool for the assessment of the mechanical properties and, possibly, also for the design of organic crystals with predetermined mechanical properties and dynamic behavior.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".