A quantum chemistry study of the first singlet(S_(1))and triplet(T_(1))excited states of phenylsulfonyl-carbazole compounds,proposed as useful thermally activated delayed fluorescence(TADF)emitters for organic light e...A quantum chemistry study of the first singlet(S_(1))and triplet(T_(1))excited states of phenylsulfonyl-carbazole compounds,proposed as useful thermally activated delayed fluorescence(TADF)emitters for organic light emitting diode(OLED)applications,was performed with the quantum Equation-Of-Motion Variational Quantum Eigensolver(qEOM-VQE)and Variational Quantum Deflation(VQD)algorithms on quantum simulators and devices.These quantum simulations were performed with double zeta quality basis sets on an active space comprising the highest occupied and lowest unoccupied molecular orbitals(HOMO,LUMO)of the TADF molecules.The differences in energy separations between S_(1) and T_(1)(ΔEST)predicted by calculations on quantum simulators were found to be in excellent agreement with experimental data.Differences of 17 and 88 mHa with respect to exact energies were found for excited states by using the qEOM-VQE and VQD algorithms,respectively,to perform simulations on quantum devices without error mitigation.By utilizing state tomography to purify the quantum states and correct energy values,the large errors found for unmitigated results could be improved to differences of,at most,4 mHa with respect to exact values.Consequently,excellent agreement could be found between values ofΔEST predicted by quantum simulations and those found in experiments.展开更多
With the growing availability of data within various scientific domains,generative models hold enormous potential to accelerate scientific discovery.They harness powerful representations learned from datasets to speed...With the growing availability of data within various scientific domains,generative models hold enormous potential to accelerate scientific discovery.They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly.We present the Generative Toolkit for Scientific Discovery(GT4SD).This extensible open-source library enables scientists,developers,and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.展开更多
基金Q.G.,M.S.,H.C.W.,E.W.,Y.O.,H.N.and N.Y.acknowledge support from MEXT Quantum Leap Flagship Program Grant Number JP-MXS0118067285 and JP-MXS0120319794。
文摘A quantum chemistry study of the first singlet(S_(1))and triplet(T_(1))excited states of phenylsulfonyl-carbazole compounds,proposed as useful thermally activated delayed fluorescence(TADF)emitters for organic light emitting diode(OLED)applications,was performed with the quantum Equation-Of-Motion Variational Quantum Eigensolver(qEOM-VQE)and Variational Quantum Deflation(VQD)algorithms on quantum simulators and devices.These quantum simulations were performed with double zeta quality basis sets on an active space comprising the highest occupied and lowest unoccupied molecular orbitals(HOMO,LUMO)of the TADF molecules.The differences in energy separations between S_(1) and T_(1)(ΔEST)predicted by calculations on quantum simulators were found to be in excellent agreement with experimental data.Differences of 17 and 88 mHa with respect to exact energies were found for excited states by using the qEOM-VQE and VQD algorithms,respectively,to perform simulations on quantum devices without error mitigation.By utilizing state tomography to purify the quantum states and correct energy values,the large errors found for unmitigated results could be improved to differences of,at most,4 mHa with respect to exact values.Consequently,excellent agreement could be found between values ofΔEST predicted by quantum simulations and those found in experiments.
基金The authors acknowledge Helena Montenegro,Yoel Shoshan,Nicolai Ree,Miruna Cretu and Helder Lopes for their open-source contributions to the GT4SD.
文摘With the growing availability of data within various scientific domains,generative models hold enormous potential to accelerate scientific discovery.They harness powerful representations learned from datasets to speed up the formulation of novel hypotheses with the potential to impact material discovery broadly.We present the Generative Toolkit for Scientific Discovery(GT4SD).This extensible open-source library enables scientists,developers,and researchers to train and use state-of-the-art generative models to accelerate scientific discovery focused on organic material design.
