aceΔ -Machine Learning for the Prediction of Metal Complex Properties
The discovery and design of novel transition metal complexes for specific applications heavily rely on computational high-throughput screenings to identify promising candidates for experimental validation. However, traditional computational approaches, such as density functional theory, are often too computationally demanding to be applied on a large scale. Machine learning methods offer a promising alternative due to their excellent computational efficiency, but their accuracy and high data requirements remain…
A delta-ML graph neural network using GFN2-xTB geometries and DFT single-points as low-fidelity inputs predicted PBE0-D3BJ and PBE-D3BJ level properties of transition metal complexes with higher accuracy and better data efficiency than a conventional benchmark.
Using cheaper low-fidelity methods incurs a trade-off of reduced predictive performance, and the approach is motivated by settings with limited training data.
Evidence
- Peer-reviewedChemistry – A European Journal2026-08-13
How should this claim be treated?
Truvace Impact Record TRV-2026-0770, v1: “Δ -Machine Learning for the Prediction of Metal Complex Properties.” Truvace, 2026-08-15. /record/TRV-2026-0770 (accessed at citation time). sha256 7747b005e0ef21e3…
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