THEORETICAL PREDICTION AND STUDIES OF SELECTED NOVEL MATERIALS UNDER AMBIENT AND EXTREME CONDITIONS
Bibliographic record
Abstract
The development of powerful computer algorithms that are specialized at exploring the energy landscape of chemical systems has revolutionized chemical physics and its derived disciplines. Such algorithms that ranges from random search to genetic algorithm are capable of uncovering a geometric configuration for a combination of chemical elements with minimum energy. The unbiased particle swarm-intelligence optimization algorithm extends the capabilities of the genetic algorithm by incorporating social intelligence through particle communication. Social communication during energy surface exploration improves the efficiency and convergence of the algorithm by preventing prediction of similar-energy structures. Particle swarm-intelligence optimization algorithm is capable of solving crystal structure problems and predicting novel crystal structures across dimensions ranging from 0D (clusters) to 3D bulk solids at specific pressure. In this study, the particle swarm-intelligence optimization algorithm was used to study and solve crystal structure problems relating to two classes of materials of industrial significance – high energy density materials and bimetallic nanoclusters. As a significant step towards solving the problem of finding a single-bonded allotrope of nitrogen, we discuss the prediction and characterization of this member of very important class of material – high energy density materials (HEDMs). A new allotrope of nitrogen formed solely by N−N single bonds is predicted to exist between 100 and 150 GPa using the metadynamics algorithm with a biased potential. The crystal structure is characterized by a distorted tetrahedral network consisting of fused N8, N10, and N12 rings. Stability of the structure is established by phonon and vibrational free energy calculations at zero and finite temperatures, respectively. The simulated x-ray diffraction pattern of the new phase is compared to the pattern of a recently synthesized nitrogen phase at the same P-T conditions and an excellent agreement is observed. This suggests the new phase is likely to form above the stability field of cubic gauche (cg) phase. The outstanding metastability of the new phase is attributed to the intrinsic stability of the sp3 bonding as well as the energetically favorable dihedral angles between N−N single bonds, in either gauche or trans conformation. The results of this work after the lab-synthesized cg phase will stimulate new research on metastable phases of nitrogen and their applications as environment-friendly HEDMs. Furthermore, in the second part of this thesis, bimetallic cluster growth is theoretically explored up to the bulk phase. Small clusters provide a unique medium between a single atom and the bulk crystal. Preliminary theoretical and experimental results show that the geometric structures and electronic properties of clusters often differ radically from those of the solid state. Here, a first-principles investigation to explore the growth mechanism of bimetallic clusters AlnAun (n=1-10) and AlAu crystal structures is carried out. It was found that the tetrahedral Al2Au2 cluster can serve as the building block to construct the subsequent nanomaterials as a function of the cluster size until the AlAu bulk. The results in this work provide a clear illustration of how structure evolve from a two-atom particle to multi-atom nanoclusters, and to 3D bulk element. Continued experimental and theoretical studies of these AlnAun clusters may lead to the discovery of how properties transform from a particle to the bulk phase which has important technological implications in electronics, engineering and catalysis.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".