Concept · chemistry
Computational chemistry
12 studies2 discoveries
Computational chemistry predicts structure, spectroscopy or barriers — most often with DFT — and is only as good as the functional, basis and the experiment it is tested against.
Students now meet DFT in every physical-chemistry course; the library shows when it explains a measured property and when it is only a model.
Studies
12
Findings
5
7 supporting · 0 challenging · 0 qualifying citations
Open tensions
1
Currently
What we know
- This library holds 12 empirical chemistry papers on computational chemistry with isolated findings, rates or spectra rather than reviews.
- Relativistic DFT shows halogen and hydrogen bonds share HOMO–LUMO covalency; halogen bonds have weaker electrostatics but stronger orbital mixing.
- Linear scaling of Suzuki intermediates yields volcano plots that place Ni/Pd/Pt near the top and coinage metals on the wrong slope.
- A Gaussian-process model that includes SNAr transition-state features predicts experimental activation energies with 0.77 kcal mol−1 MAE, beating raw DFT.
- TD-DFT AIMD and wavelet spectra show GFP ESPT is concerted once low-frequency chromophore modes planarize the H-bond wire.
Largest unresolved question
Functional choice and static vs dynamics treatments change spin states, barriers and spectra; agreement on one molecule does not transfer automatically.
Common misconceptions
DFT is exact quantum mechanics.
Kohn–Sham DFT is an approximate electronic-structure model that must be benchmarked.
Related
Claim ledger
What the evidence shows
Drawn from 12 studies in this library. Mix labels say which citation roles are present; they are not a strength score. Supports means evidence for a finding; Challenges means evidence against a stated position; Qualifies marks scope.
This library holds 12 empirical chemistry papers on computational chemistry with isolated findings, rates or spectra rather than reviews.
- Halogen bonds are not purely electrostatic
- Volcano plots for homogeneous Suzuki catalysts
- ML plus transition states predict SNAr barriers
Study Role Design N Population Outcome Halogen bonds are not purely electrostatic Supports Computational / modellingZORA-BP86/TZ2P energy decomposition of trihalide halogen bonds vs hydrogen bonds Molecular orbital theory study — no sample N DX···A− and DH···A− model complexes (D, X, A = F–I) Electrostatic vs orbital contributions distinguishing halogen and hydrogen bonds Volcano plots for homogeneous Suzuki catalysts Supports Computational / modellingDFT free-energy scaling relations and volcano plots for a Suzuki catalytic cycle across ML2 complexes Homogeneous-catalysis computational screen — no sample N ML2 catalyst models in a Suzuki coupling cycle Scaling relationships identifying near-ideal oxidative-addition energetics ML plus transition states predict SNAr barriers Supports Computational / modellingDFT/physical-organic descriptors with GPR for SNAr barriers and selectivity ML on reaction barriers/selectivity; learning curves emphasize 50–150 training samples — no single cohort N Nucleophilic aromatic substitution reactions (including patent-selectivity cases) Predicted activation free energies and regio-/chemoselectivity accuracy Relativistic DFT shows halogen and hydrogen bonds share HOMO–LUMO covalency; halogen bonds have weaker electrostatics but stronger orbital mixing.
Linear scaling of Suzuki intermediates yields volcano plots that place Ni/Pd/Pt near the top and coinage metals on the wrong slope.
A Gaussian-process model that includes SNAr transition-state features predicts experimental activation energies with 0.77 kcal mol−1 MAE, beating raw DFT.
TD-DFT AIMD and wavelet spectra show GFP ESPT is concerted once low-frequency chromophore modes planarize the H-bond wire.
Debates
Tensions and limits
Some items are genuine disagreements on the same question. Others mark different assays, populations, or outcomes.
Functional choice and static vs dynamics treatments change spin states, barriers and spectra; agreement on one molecule does not transfer automatically.
Functional choice and static vs dynamics treatments change spin states, barriers and spectra; agreement on one molecule does not transfer automatically.
Study Role Design N Population Outcome Halogen bonds are not purely electrostatic Supports Computational / modellingZORA-BP86/TZ2P energy decomposition of trihalide halogen bonds vs hydrogen bonds Molecular orbital theory study — no sample N DX···A− and DH···A− model complexes (D, X, A = F–I) Electrostatic vs orbital contributions distinguishing halogen and hydrogen bonds Volcano plots for homogeneous Suzuki catalysts Supports Computational / modellingDFT free-energy scaling relations and volcano plots for a Suzuki catalytic cycle across ML2 complexes Homogeneous-catalysis computational screen — no sample N ML2 catalyst models in a Suzuki coupling cycle Scaling relationships identifying near-ideal oxidative-addition energetics
PaperFren reads this as a limit on how far one study travels — different assays, populations, or outcomes — not a forced fight between papers.
Timeline
How understanding moved
Study years are when the paper was published. Evidence edits are dated changes to this page's claims. Explanations are when PaperFren added a Discovery — not a claim that the science happened that day.
2026
Concept page published
Computational chemistry
- A neural network predicted spin states well — and reported where it should not be trusted
- Halogen bonds are not purely electrostatic — the orbital term does real work
Change log
What changed
Dated edits to this page's evidence: studies added or removed from a claim, claims added or withdrawn, and new explanations tagged here. Coverage edits are distinct from reviewed research changes and corrections. Dates record PaperFren updates; study publication dates appear in the timeline.
- Halogen bonds are not purely electrostatic — the orbital term does real workEvidence: Strengthening
- Coverage updated · Concept page published
Papers
12 studies in this library bear on Computational chemistry, ordered by citations.
- Halogen bonds are not purely electrostatic
Relativistic DFT shows halogen and hydrogen bonds share HOMO–LUMO covalency; halogen bonds have weaker electrostatics but stronger orbital mixing.
- DFT: graphene SACs break CO2 scaling relations
Single metal atoms in graphene vacancies are predicted to reduce CO2 to methanol or methane at milder limiting potentials than metal terraces by weakening *CO more than *CHO.
- Anion–π contacts are common in the PDB
A PDB-wide search finds anion–π interactions in most protein structures, with Asp/Glu carboxylates packing on aromatics and frequent cation–π partners opposite.
- Intramolecular vibrations relax a molecular magnet
CASSCF plus lattice dynamics on [(tpaPh)Fe]− show acoustic phonons are silent; intramolecular modes modulate D far more than rotations.
- Neural nets predict TM spin states and bonds
Graph-based ANNs trained on 2690 DFT geometries of Cr–Ni octahedral complexes predict spin splitting and metal–ligand distances without 3D coordinates.
- ML plus transition states predict SNAr barriers
A Gaussian-process model that includes SNAr transition-state features predicts experimental activation energies with 0.77 kcal mol−1 MAE, beating raw DFT.
- Defect hydroxides in UiO-66 shuttle protons
Simulations of missing-linker UiO-66 put charge-balancing OH on under-coordinated Zr and show those sites swap between hydroxide and water by rapid proton transfer with extra-framework water.
- Modelled peptides inhibit SARS-CoV-2 main protease
Simulations prefer a neutral His41/Cys145 dyad and guided four designed peptides that bind the Mpro dimer and inhibit substrate cleavage with IC50 values of 3–5 μM.
Show 4 more studiesShow fewer studies
- Volcano plots for homogeneous Suzuki catalysts
Linear scaling of Suzuki intermediates yields volcano plots that place Ni/Pd/Pt near the top and coinage metals on the wrong slope.
- AIMD maps GFP's excited-state proton shuttle
TD-DFT AIMD and wavelet spectra show GFP ESPT is concerted once low-frequency chromophore modes planarize the H-bond wire.
- Hammett σ from composition predicts SN2 barriers
A data-enhanced Hammett model maps SN2 barrier heights across substituent and leaving-group space using additive σ values that depend only on group identity and distance to the reacting carbon.
- NN potentials map gold–water ORR paths
Equivariant neural-network potentials drive nanosecond metadynamics showing associative ORR on Au(100) in which *OOH is reduced to two *OH, with a ~0.3 eV barrier.
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Select 2–10 studies. Design and N are labels, not a ranking.
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Questions
What is still open
Functional choice and static vs dynamics treatments change spin states, barriers and spectra; agreement on one molecule does not transfer automatically.
Ask PaperFren about Computational chemistry
Study this conceptflashcards and short-answer questions
What does computational chemistry mean in this chemistry library?
Computational chemistry predicts structure, spectroscopy or barriers — most often with DFT — and is only as good as the functional, basis and the experiment it is tested against.
Name one empirical finding from the computational chemistry papers.
Relativistic DFT shows halogen and hydrogen bonds share HOMO–LUMO covalency; halogen bonds have weaker electrostatics but stronger orbital mixing.
What is a limit of computational chemistry evidence here?
Functional choice and static vs dynamics treatments change spin states, barriers and spectra; agreement on one molecule does not transfer automatically.