Supporting information (part I): BGF structures and energies for MoS₂ ReaxFF training (Ostadhossein et al.)
Non-primary PDF
Supporting Information package I for the JPCL ReaxFF letter on MoS\(_2\) (Ostadhossein_MoS2_JPC_Letters_2017.pdf). Listed in maintainer style as SI-primary ingest in docs/corpus/NON_PRIMARY_ARTICLE_PAPER_SLUGS.md (section A pattern). Scientific interpretation belongs with [[2017ostadhossein-venue-research]].
Summary¶
This PDF archives BIOGRF structure blocks and single-point energies (kcal/mol) for small Mo–S–H clusters used in the MoS\(_2\) ReaxFF optimization workflow (examples named in the extract include MoS\(_6\), MoS(SH)\(_2\)H, MoS\(_2\)SHH, MoS\(_4\)H\(_2\)). The file supplies coordinates, connectivity, and reference energies paired to the training pipeline described in the parent letter and companion SI parts. It is not a standalone research article: claims about MoS\(_2\) edge energetics, catalysis, or MD validation appear in the main JPCL text.
For parameterization audits, the value of this ingest is transparency: each cluster entry ties a specific connectivity pattern to a reference energy used in the objective function during genetic or least-squares optimization. That pairing is what downstream reviewers need when asking whether edge, vacancy, or hydrogenated motifs were balanced against one another during the fit.
Methods¶
Force-field training / fitting. This Supporting Information (Part I) PDF lists BIOGRF structure blocks and reference energies (kcal/mol) for small Mo–S–H clusters used in the MoS₂ ReaxFF optimization reported in [[2017ostadhossein-venue-research]]. Entries pair connectivity, coordinates, and single-point energies that enter the published least-squares / genetic objective together with Table S1/S2 and trainset.in weights in 2017ostadhossein-venue-microsoft-word-2 / 2017ostadhossein-venue-microsoft-word-3.
MD application (atomistic dynamics). N/A — this file is training data only; production MD validation trajectories are described in the parent JPCL letter.
Static QM / DFT. QM levels, basis/cutoff conventions, and DFT benchmarks for the same training set are documented on [[2017ostadhossein-venue-microsoft-word-2]] and in the main text of [[2017ostadhossein-venue-research]].
Review / non-simulation framing. Non-primary SI artifact; scientific interpretation and DOI (10.1021/acs.jpclett.6b02902) belong on [[2017ostadhossein-venue-research]].
Findings¶
Outcomes. The SI supplies the numerical cluster training set required to audit the published ReaxFF fit; it does not add standalone mechanistic conclusions beyond supporting the parent publication.
Comparisons. Each motif ties a BIOGRF structure to a reference energy used alongside DFT benchmarks summarized on the VOR page—use those tables when recomputing training residuals.
Sensitivity / design levers. Relative weights and which bond/angle motifs appear in Part I determine how much each Mo–S–H fragment constrains the fit; see Part III for explicit per-observable weights.
Limitations / outlook. When porting snippets to LAMMPS data decks, verify periodic images and charge initialization against the parent letter examples—cluster training geometries are not always literal production supercells.
Corpus honesty. SI-only ingest; cite the JPCL article for scholarly claims. Filename reflects Microsoft Word export history, not content type.
Limitations¶
Filename reflects Microsoft Word export; cite the parent letter DOI for scholarly references. Frontmatter doi is empty here—use [[2017ostadhossein-venue-research]] for DOI 10.1021/acs.jpclett.6b02902 (verify on the VOR PDF).
Relevance to group¶
Training-data artifact for Penn State MoS\(_2\) ReaxFF development.
Citations and evidence anchors¶
- Parent letter:
[[2017ostadhossein-venue-research]](JPCL; DOI on that page). SI use. Copy BIOGRF blocks and energies from this PDF into the same ReaxFF optimization toolchain described in[[2017ostadhossein-venue-research]]; cite the parent JPCL letter for scientific interpretation.
Reproducibility and corpus locators¶
This note documents where to find primary evidence in-repo; it does not add new scientific claims beyond the cited publication.
Normalized layer. When present, normalized/papers/{slug}.json mirrors manifest hashes, bibliography fields, and extraction pointers; if pdf_path or PDF bytes change, follow AGENTS.md and docs/PHASE3_RUNBOOK.md to re-profile rather than editing PDFs in place.
Authority chain. For numerical settings (cutoffs, timesteps, ensembles, kinetics), use the peer-reviewed PDF (and publisher Supporting Information) as the authoritative source; this wiki summarizes for navigation and retrieval.