Hannes Kneiding

• Kneiding, Hannes; Nova Flores, Ainara & Balcells Badia, David (2024). Directional multiobjective optimization of metal complexes at the billion-system scale. Nature Computational Science. ISSN 2662-8457. doi: 10.1038/s43588-024-00616-5.
• Kneiding, Hannes; Nova Flores, Ainara & Balcells Badia, David (2023). Directional Multiobjective Optimization of Metal Complexes at the Billion-Scale with the tmQMg-L Dataset and PL-MOGA Algorithm. ChemRxiv. ISSN 2573-2293. doi: 10.26434/chemrxiv-2023-k3tf2-v2. Vis sammendrag

  Transition metal complexes (TMCs) play a key role in several areas of high interest, including medicinal chemistry, renewable energies, and nanoporous materials. The development of TMCs enabling these technologies remains challenged by the need to optimize multiple properties within very large chemical spaces, in which the thirty transition metals can be combined with a virtually infinite number of ligands. In this work, we provide the open tmQMg-L dataset including 30K TMC ligands, which combines large chemical diversity with synthesizability. The charge and metal-coordination mode of the ligands were robustly defined with a novel algorithm based on graph and natural bond orbital theories. The tmQMg-L dataset was leveraged in the automated generation of 1.37M TMCs resulting from all possible combinations between a square planar palladium(II) scaffold and a pool of 50 different ligands. This TMC space was used to benchmark a multiobjective genetic algorithm (MOGA) that optimized two properties over a Pareto front; namely the polarizability (alpha) and the HOMO-LUMO gap (epsilon). The MOGA evolved 130 TMC hits with maximal (alpha, epsilon) values in a way that could be easily rationalized by analyzing the nature of the ligands selected. Instead of the traditional mutation and crossover of fragments within a single ligand, this MOGA implemented full-ligand genetic operations acting on all coordination sites, maximizing chemical diversity. Further, we extended this MOGA algorithm with the Pareto-Lighthouse functionality (PL-MOGA), which allows for controlling both the aim and scope of the multiobjective optimization over the Pareto front. In explicit spaces containing billions of TMCs, the PL-MOGA enabled the explainable generation of thousands of novel and highly diverse TMC hits. We believe that the combined use of the tmQMg-L dataset and PL-MOGA algorithm will facilitate the discovery of TMCs with optimal properties within untapped chemical spaces.

• Kneiding, Hannes; Lukin, Ruslan; Lang, Lucas; Reine, Simen; Pedersen, Thomas Bondo & De Bin, Riccardo [Vis alle 7 forfattere av denne artikkelen] (2023). Deep learning metal complex properties with natural quantum graphs. Digital Discovery. ISSN 2635-098X. 2(3), s. 618–633. doi: 10.1039/d2dd00129b. Fulltekst i vitenarkiv Vis sammendrag

  Machine learning can make a strong contribution to accelerating the discovery of transition metal complexes (TMC). These compounds will play a key role in the development of new technologies for which there is an urgent need, including the production of green hydrogen from renewable sources. Despite the recent developments in machine learning for drug discovery and organic chemistry in general, the application of these methods to TMCs remains challenged by their higher complexity and the limited availability of large datasets. In this work, we report a representation for deep graph learning on TMCs – the natural quantum graph (NatQG), which leverages the electronic structure data available from natural bond orbital (NBO) analysis. This data was used to define both the topology and the information expressed by the NatQG graphs. At the topology level, two different NatQG flavors were developed: u-NatQG, with undirected edges, and d-NatQG, with edges directed along donor → acceptor orbital interactions. At the information level, the node and edge attribute vectors of both graphs contain NBO data, including natural charges and bond orders. The NatQG graphs were used to develop graph neural networks (GNNs) for the prediction of the quantum properties underlying the structure and reactivity of TMCs (e.g. HOMO–LUMO gap and polarizability). These models surpassed baselines based on traditional descriptors and performed at a level similar to, or higher than, state-of-the-art GNNs based on radial cutoffs. The results showed that the electronic structure information encoded by the models has a stronger impact on its accuracy than the geometric information. With the aim of benchmarking the GNNs, we also developed the transition metal quantum mechanics graph dataset (tmQMg), which provides the geometries, properties, and NatQG graphs of 60k TMCs.

• Kneiding, Hannes; Lukin, Ruslan & Balcells Badia, David (2022). Deep Learning Metal Complex Properties with Natural Quantum Graphs. ChemRxiv. ISSN 2573-2293. doi: 10.26434/chemrxiv-2022-fd43k.

Se alle arbeider i Cristin

• Burnage, Arron Luc; Kneiding, Hannes; Nova, Ainara & Balcells, David (2023). A 3-Dimentional Search for a Pd Cross-Coupling Catalyst for the Suzuki-Miyaura Reaction .
• Kneiding, Hannes; Nova Flores, Ainara & Balcells Badia, David (2023). Evolutionary Multiobjective Optimization for Transition Metal Complexes.
• Kneiding, Hannes; Nova Flores, Ainara & Balcells Badia, David (2023). Evolutionary Multiobjective Optimization for Transition Metal Complexes.
• Kneiding, Hannes & Balcells Badia, David (2023). Machine Learning Quantum Properties of Transition Metal Complexes with Natural Quantum Graphs.
• Kneiding, Hannes & Balcells Badia, David (2022). Graph Representation Learning for Transition Metal Complexes.
• Kneiding, Hannes & Balcells Badia, David (2022). Machine Learning Quantum Properties of Transition Metal Complexes from Graph Representations.

Se alle arbeider i Cristin