Fragment-Based QM/MM Method for Modeling Molecular Crystals and Clusters
Bibliographic record
Abstract
Molecular aggregates like molecular crystals and clusters findimportant applications as pharmaceutical drugs, explosives, organicsemi-conductors, materials for fuel storage, etc. These systems aredominated by a variety of intermolecular interactions of differentstrengths like hydrogen bonding, dispersion, electrostatics andinduction. Traditional classical force field methods for studying theproperties of these chemical systems lack the desirable accuracy fortreatment of these different types of intermolcular interactions,while efficient treatment with electronic structure methods likesecond-order perturbative Moller-Plesset (MP2) and coupled clustermethods are unaffordable for these large chemical systems. Methodsbased on density functional theory (DFT) suffer from their inabilityto be systematically improvable. Hence, alternative methods aredesirable for electronic structure quality predictions while beingcomputationally affordable for these molecular crystals and clusters.The Hybrid Many-Body Interaction (HMBI) method described in thisdissertation has been developed for studying the properties of thesemolecular aggregates. In this method, the system is broken down intofragments and the most important short-range interactions are treatedusing highly accurate electronic structure methods while the lessimportant but more expensive interfragment interactions are treatedusing inexpensive classical force fields. Here, we demonstrate thatthe HMBI predictions are electronic structure quality while beingcomputationally affordable. Moreover, these predictions can besystematically improved by use of more accurate electronic structuremethods and force fields.Here, the HMBI method has been employed in predicting the energeticsand structure of molecular crystals and clusters. Some othercapabilities of this method include prediction of the crystalstructure in the presence of external stress, vibrational spectra,phonon dispersion curves, thermal properties like sublimation heatsand specific heat capacities and elastic constants. We demonstratethat accurate HMBI predictions of these crystal properties allows foraccurate identification and screening of different crystal polymorphswhich is important in various applications of these materials.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".