Kneiding Hannes, Nova Ainara, Balcells David
Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, Oslo, Norway.
Centre for Materials Science and Nanotechnology, Department of Chemistry, University of Oslo, Oslo, Norway.
Nat Comput Sci. 2024 Apr;4(4):263-273. doi: 10.1038/s43588-024-00616-5. Epub 2024 Mar 29.
The discovery of transition metal complexes (TMCs) with optimal properties requires large ligand libraries and efficient multiobjective optimization algorithms. Here we provide the tmQMg-L library, containing 30k diverse and synthesizable ligands with robustly assigned charges and metal coordination modes. tmQMg-L enabled the generation of 1.37 million palladium TMCs, which were used to develop and benchmark the Pareto-Lighthouse multiobjective genetic algorithm (PL-MOGA). With fine control over aim and scope, this algorithm maximized both the polarizability and highest occupied molecular orbital-lowest unoccupied molecular orbital gap of the TMCs within selected regions of the Pareto front, without requiring prior knowledge on the objective limits. Instead of genetic operations on small ligand fragments, the PL-MOGA did whole-ligand mutation and crossover operations, which in chemical spaces containing billions of systems, yielded thousands of highly diverse TMCs in an interpretable manner.
发现具有最佳性能的过渡金属配合物(TMC)需要大量的配体库和高效的多目标优化算法。在此,我们提供了tmQMg-L库,其中包含30,000种不同且可合成的配体,其电荷和金属配位模式已得到可靠确定。tmQMg-L库能够生成137万个钯TMC,这些TMC被用于开发和测试帕累托灯塔多目标遗传算法(PL-MOGA)。通过对目标和范围的精细控制,该算法在帕累托前沿的选定区域内最大化了TMC的极化率和最高占据分子轨道-最低未占据分子轨道能隙,而无需事先了解目标极限。PL-MOGA不是对小的配体片段进行遗传操作,而是进行全配体突变和交叉操作,在包含数十亿个体系的化学空间中,以可解释的方式产生了数千种高度多样的TMC。