A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies.This approach is a promising trade-off between the accuracy of ab initio meth...A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies.This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches,strategically combined with chemical intuition.The results demonstrate that the chemical properties of MOFs are indeed predictable(stochastically,not deterministically)using machine learning methods and automated analysis protocols,with the accuracy of predictions increasing with sample size.Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.展开更多
The affiliation details for George E.Froudakis were incorrect in this article.The correct affiliation details for this author are given below:Department of Chemistry,University of Crete,Voutes Campus,GR-70013 Heraklio...The affiliation details for George E.Froudakis were incorrect in this article.The correct affiliation details for this author are given below:Department of Chemistry,University of Crete,Voutes Campus,GR-70013 Heraklion,Crete,Greece This has now been corrected in the HTML and PDF versions of this article.展开更多
基金co-financed by the European Union(European Social Fund-ESF)Greek national funds through the Operational Program“Education and Lifelong Learning”of the National Strategic Reference Framework(NSRF)-Research Funding Program:THALESco-financed by AFOSR/EOARD under grant numberFA9550-15-1-0291.
文摘A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies.This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches,strategically combined with chemical intuition.The results demonstrate that the chemical properties of MOFs are indeed predictable(stochastically,not deterministically)using machine learning methods and automated analysis protocols,with the accuracy of predictions increasing with sample size.Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.
文摘The affiliation details for George E.Froudakis were incorrect in this article.The correct affiliation details for this author are given below:Department of Chemistry,University of Crete,Voutes Campus,GR-70013 Heraklion,Crete,Greece This has now been corrected in the HTML and PDF versions of this article.
