摘要
Accurate computation of the gas adsorption properties of MOFs is usually bottlenecked by the DFT calculations required to generate partial atomic charges.Therefore,large virtual screenings of MOFs often use the QEq method which is rapid,but of limited accuracy.Recently,machine learning(ML)models have been trained to generate charges in much better agreement with DFT-derived charges compared to the QEq models.Previous ML charge models for MOFs have all used training sets with less than 3000 MOFs obtained from the CoREMOF database,which has recently been shown to have high structural error rates.
基金
Financial support from the Natural Sciences and Engineering Research Council of Canada(DISCOVERY Grant),the University of Ottawa,MITACS(Accelerate),and Total Energies is greatly appreciated,as well as the computing resources provided by Total Energies,University of Ottawa,and the Digital Research Alliance of Canada.
