Reversible oxygen sensing abilities based on blue and orange photoluminescence in the solid state are achieved by using newly synthesized copper(I)complexes bearing diimine and dodecafluorinated diphosphine ligands.We...Reversible oxygen sensing abilities based on blue and orange photoluminescence in the solid state are achieved by using newly synthesized copper(I)complexes bearing diimine and dodecafluorinated diphosphine ligands.We found that the blue emission of[Cu(dmp)(dfppe)]PF_(6)(dmp=2,9-dimethyl-1,10-phenanthroline,dfppe=1,2-bis[bis(pentafluorophenyl)phosphino]ethane)in the solid state is very strong under argon,while it is nearly invisible under air.展开更多
In this study,we explore the potential of using quantum natural language processing(QNLP)for property-guided inverse design of metal-organic frameworks(MOFs)with targeted properties.Specifically,by analyzing 450 hypot...In this study,we explore the potential of using quantum natural language processing(QNLP)for property-guided inverse design of metal-organic frameworks(MOFs)with targeted properties.Specifically,by analyzing 450 hypothetical MOF structures consisting of 3 topologies,10 metal nodes and 15 organic ligands,we categorize these structures into four distinct classes for pore volume and CO_(2)Henry’s constant values.We then compare various QNLP models(i.e.,the bag-of-words,DisCoCat(Distributional Compositional Categorical),and sequence-based models)to identify the most effective approach to process the MOF dataset.Using a classical simulator provided by the IBM Qiskit,the bag-of-words model is identified to be the optimum model,achieving validation accuracies of 88.6%and 78.0%for binary classification tasks on pore volume and CO_(2)Henry’s constant,respectively.Further,we developed multi-class classification models tailored to the probabilistic nature of quantum circuits,with average test accuracies of 92%and 80%across different classes for pore volume and CO_(2)Henry’s constant datasets.Finally,the performance of generating MOF with target properties showed accuracies of 97.75%for pore volume and 90%for CO_(2)Henry’s constant,respectively.Although our investigation covers only a fraction of the vast MOF search space,it marks a promising first step towards using quantum computing for materials design,offering a new perspective through which to explore the complex landscape of MOFs.展开更多
基金supported by JSPS KAKENHI Grant Number JP16 K17881,the Iketani Science and Technology Foundation,and a grant from the Faculty of Science and Technology,Seikei University.
文摘Reversible oxygen sensing abilities based on blue and orange photoluminescence in the solid state are achieved by using newly synthesized copper(I)complexes bearing diimine and dodecafluorinated diphosphine ligands.We found that the blue emission of[Cu(dmp)(dfppe)]PF_(6)(dmp=2,9-dimethyl-1,10-phenanthroline,dfppe=1,2-bis[bis(pentafluorophenyl)phosphino]ethane)in the solid state is very strong under argon,while it is nearly invisible under air.
基金National Research Foundation of Korea(Project Number RS-2024-00337004)for the financial support.
文摘In this study,we explore the potential of using quantum natural language processing(QNLP)for property-guided inverse design of metal-organic frameworks(MOFs)with targeted properties.Specifically,by analyzing 450 hypothetical MOF structures consisting of 3 topologies,10 metal nodes and 15 organic ligands,we categorize these structures into four distinct classes for pore volume and CO_(2)Henry’s constant values.We then compare various QNLP models(i.e.,the bag-of-words,DisCoCat(Distributional Compositional Categorical),and sequence-based models)to identify the most effective approach to process the MOF dataset.Using a classical simulator provided by the IBM Qiskit,the bag-of-words model is identified to be the optimum model,achieving validation accuracies of 88.6%and 78.0%for binary classification tasks on pore volume and CO_(2)Henry’s constant,respectively.Further,we developed multi-class classification models tailored to the probabilistic nature of quantum circuits,with average test accuracies of 92%and 80%across different classes for pore volume and CO_(2)Henry’s constant datasets.Finally,the performance of generating MOF with target properties showed accuracies of 97.75%for pore volume and 90%for CO_(2)Henry’s constant,respectively.Although our investigation covers only a fraction of the vast MOF search space,it marks a promising first step towards using quantum computing for materials design,offering a new perspective through which to explore the complex landscape of MOFs.
