New tools enable new ways of working,and materials science is no exception.In materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes dri...New tools enable new ways of working,and materials science is no exception.In materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes driven by Artificial Intelligence (AI),simulation and experimental automation.In this perspective,we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle.We show,using the example of the development of a novel chemically amplified photoresist,how these technologies’ impacts are amplified when they are used in concert with each other as powerful,heterogeneous workflows.展开更多
The ground and excited state calculations at key geometries, such as the Frank–Condon (FC) and the conical intersection (CI)geometries, are essential for understanding photophysical properties. To compute these geome...The ground and excited state calculations at key geometries, such as the Frank–Condon (FC) and the conical intersection (CI)geometries, are essential for understanding photophysical properties. To compute these geometries on noisy intermediate-scalequantum devices, we proposed a strategy that combined a chemistry-inspired spin-restricted ansatz and a new excited statecalculation method called the variational quantum eigensolver under automatically-adjusted constraints (VQE/AC). Unlike theconventional excited state calculation method, called the variational quantum deflation, the VQE/AC does not require the pre-determination of constraint weights and has the potential to describe smooth potential energy surfaces. To validate this strategy,we performed the excited state calculations at the FC and CI geometries of ethylene and phenol blue at the complete active spaceself-consistent field (CASSCF) level of theory, and found that the energy errors were at most 2 kcal mol−1 even on the ibm_kawasakidevice.展开更多
文摘New tools enable new ways of working,and materials science is no exception.In materials discovery,traditional manual,serial,and human-intensive work is being augmented by automated,parallel,and iterative processes driven by Artificial Intelligence (AI),simulation and experimental automation.In this perspective,we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle.We show,using the example of the development of a novel chemically amplified photoresist,how these technologies’ impacts are amplified when they are used in concert with each other as powerful,heterogeneous workflows.
基金This work was supported by JSPS KAKENHI Grant no.JP17H06445,20K05438,and JST Gannt no.JPMJPF2221.We also acknowledge the computer resources provided by the Academic Center for Computing and Media Studies(ACCMS)at Kyoto University and by the Research Center of Computer Science(RCCS)at the Institute for Molecular Science.
文摘The ground and excited state calculations at key geometries, such as the Frank–Condon (FC) and the conical intersection (CI)geometries, are essential for understanding photophysical properties. To compute these geometries on noisy intermediate-scalequantum devices, we proposed a strategy that combined a chemistry-inspired spin-restricted ansatz and a new excited statecalculation method called the variational quantum eigensolver under automatically-adjusted constraints (VQE/AC). Unlike theconventional excited state calculation method, called the variational quantum deflation, the VQE/AC does not require the pre-determination of constraint weights and has the potential to describe smooth potential energy surfaces. To validate this strategy,we performed the excited state calculations at the FC and CI geometries of ethylene and phenol blue at the complete active spaceself-consistent field (CASSCF) level of theory, and found that the energy errors were at most 2 kcal mol−1 even on the ibm_kawasakidevice.
