Luca Cibinel

• Cibinel, Luca; Meggio, Claudio & Zuccato, Lorenzo (2026). Enhancing Quantum Graph Representations via Heuristics and Model-Based Clustering. I Bin, Riccardo De; Salucci, Clara Bertinelli & Hubin, Aliaksandr (Red.), Proceedings of the 40th International Workshop on Statistical Modelling 28.06 – 03.07.2026, Oslo, Norway. International Workshop on Statistical Modelling. ISSN 9788255325000. Fulltekst i vitenarkiv Vis sammendrag

  We propose a novel class of representations for graph-structured data, with the objective of improving feature prediction performance. We focus on graphs with clearly identifiable and interpretable subgraphs, such as transition metal complexes. Our approach is based on the identification of subgraph popu- lations whose citizens exhibit a similar statistical behaviour. Knowledge of these populations is injected in the original graphs in the form of virtual nodes to make global information available at the local level.

• Cibinel, Luca; Linjordet, Trond; Pensar, Johan; Badia, David Balcells; Bin, Riccardo De & Ell, Basil (2026). tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes. Journal of Chemical Information and Modeling. ISSN 1549-9596. 66(13), s. 7524–7538. doi: 10.1021/acs.jcim.6c01281. Fulltekst i vitenarkiv Vis sammendrag

  Transition metal complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one in which machine learning and computational approaches can offer a substantial aid. For this reason, appropriate and accessible data sets, collecting a wide range of information, are required in order to facilitate the effective analysis and investigation of such compounds. This paper contributes to the data modeling effort via the introduction of the transition metal quantum mechanics RDF (tmQM-RDF) data set, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 60k TMCs. These descriptions are both qualitative and quantitative, encompassing the compositional nature of TMCs as well as the entirety of their molecular graphs. The Python package tmqmrdfdata is released together with the knowledge graph to provide a user-friendly interface. An example of the power of the proposed representation is presented, showcasing how the information available in tmQM-RDF can be exploited for TMC manipulation tasks, achieving promising performance even with relatively simple probabilistic models.

Se alle arbeider i NVA

• Cibinel, Luca; Meggio, Claudio & Zuccato, Lorenzo (2026). Enhancing Quantum Graph Representations via Heuristics and Model-Based Clustering. Fulltekst i vitenarkiv Vis sammendrag

  We propose a novel class of representations for graph-structured data, with the objective of improving feature prediction performance. We focus on graphs with clearly identifiable and interpretable subgraphs, such as transition metal complexes. Our approach is based on the identification of subgraph popu- lations whose citizens exhibit a similar statistical behaviour. Knowledge of these populations is injected in the original graphs in the form of virtual nodes to make global information available at the local level.

Se alle arbeider i NVA