Computational chemistry
Neural nets predict TM spin states and bonds
Open access · cc by · source: Europe PMC
Graph-based ANNs trained on 2690 DFT geometries of Cr–Ni octahedral complexes predict spin splitting and metal–ligand distances without 3D coordinates.
Study at a glance
- Design
- Computational / modelling — ANN trained on DFT octahedral Cr–Ni complexes to predict spin states and bond lengths
- N
- N=2690 · 2,690 DFT geometry optimizations after filtering spin-contaminated cases
- Population
- Homoleptic and heteroleptic octahedral transition-metal complexes
- Outcome
- Predicted spin-state energetics and metal–ligand bond lengths
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Spin-state RMSE is 3.0 (train) and 3.1 kcal mol–1 (test); ground state is right in 98% of test cases (528/538). Bond-length RMSE is 0.02 Å (LS) and 0.03 Å (HS test). On 35 CSD complexes MUE is 10 kcal mol–1 overall and 5 kcal mol–1 inside a Euclidean reliability cutoff; GGA-to-hybrid slope correction brings CSD MUE to 5 kcal mol–1.
Methodology
They DFT-optimized homoleptic and heteroleptic octahedral complexes of Cr–Ni at 0–30% HF exchange, kept 2690 jobs after dropping spin-contaminated cases, and trained dropout ANNs on connectivity descriptors (connecting atom, ligand charge, denticity, metal/oxidation state). Test split was 40%; a CSD set checked transfer.
Limitations
The model is for octahedral first-row complexes with the ligand set used in training; cyclams and early metals are outliers (~30 kcal mol–1). Descriptors ignore 3D interligand clashes. Predictions target DFT (B3LYP-family) numbers, not experiment directly.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
Neural nets can learn DFT spin-state gaps for a defined ligand set. On octahedral Cr–Ni complexes, test RMSE was 3.1 kcal mol⁻¹ and ground state was right in 528/538 test cases; CSD complexes were worse (MUE 10 kcal mol⁻¹ overall, 5 inside a reliability cutoff), and cyclams and early metals were outliers around 30 kcal mol⁻¹. Predictions target the B3LYP-family DFT the network was trained on.
Evidence for the claim as stated.
These papers do not agree on what DFT is for. One trains a statistical model to correct DFT barriers; one trains a net to emulate DFT spin states; others use a single-point or periodic calculation to rationalise a crystal, a gas-phase ion, or a ligand redox event. Quoting a kcal mol⁻¹ figure without saying whether it is raw DFT, a GP correction, or an NNP barrier mixes those jobs.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
These papers do not agree on what DFT is for. One trains a statistical model to correct DFT barriers; one trains a net to emulate DFT spin states; others use a single-point or periodic calculation to rationalise a crystal, a gas-phase ion, or a ligand redox event. Quoting a kcal mol⁻¹ figure without saying whether it is raw DFT, a GP correction, or an NNP barrier mixes those jobs.
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