Machine learning prediction of NMR shifts for rare and transition metal complexes (45Sc, 49Ti, 89Y, 91Zr, 139La)

2025/08/28 by Yaroslav I. Isaev, Dmitry M. Makarov, Ilya A. Khodov · 1 voice · 1 citation
Chemistry · Materials Science · Medicine · #Advanced NMR Techniques and Applications #Machine Learning in Materials Science #Medical Imaging Techniques and Applications

paper · doi:10.1016/j.molliq.2025.128417

openalex publication_date 2025/08/28 · crossref created 2025/08/28 · openalex created_date 2025/10/10 · crossref issued 2025/11/01 · crossref published 2025/11/01 · crossref published-print 2025/11/01 · crossref deposited 2026/05/26 · crossref indexed 2026/07/17 · openalex updated_date 2026/07/23

Cited by

• Efficient Prediction of Transition-Metal NMR Chemical Shifts Using Machine Learning: Do Two-Dimensional Descriptors Suffice?

Discussions

• New study in Journal of Molecular Liquids: ML predicts NMR chemical shifts for metal complexes (45Sc, 49Ti, 89Y, 91Zr, 139La). CatBoost+RDKit ≈7% RMSE (Sc/Y/La); SHAP highlights key features. Read: do [bsky, 5 points, 0 comments]