Coupled electrochemical systems for the direct capture and conversion of CO have garnered significant attention owing to their potential to enhance energy-and cost-efficiency by circumventing the amine regeneration st...Coupled electrochemical systems for the direct capture and conversion of CO have garnered significant attention owing to their potential to enhance energy-and cost-efficiency by circumventing the amine regeneration step.However,optimizing the coupled system is more challenging than handling separated systems because of its complexity,caused by the incorporation of solvent and heterogeneous catalysts.Nevertheless,the deployment of machine learning can be immensely beneficial,reducing both time and cost owing to its ability to simulate and describe complex systems with numerous parameters involved.In this review,we summarized the machine learning techniques employed in the development of CO_(2)capture solvents such as amine and ionic liquids,as well as electrochemical CO_(2)conversion catalysts.To optimize a coupled electrochemical system,these two separately developed systems will need to be combined via machine learning techniques in the future.展开更多
We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do.While the error rate is high,generative AI seems to be able to effectively structure vast...We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do.While the error rate is high,generative AI seems to be able to effectively structure vast amounts of scientific knowledge and provide interesting and testable hypotheses.The future scientific enterprise may include synergistic efforts with a swarm of“hypothesis machines”,challenged by automated experimentation and adversarial peer reviews.展开更多
基金supported by a grant from the National Research Foundation of Korea(NRF)funded by the Korean government,Ministry of Science and ICT(MSIT)(No.2021R1A6A3A01086766,2021R1A6 A3A03044878)supported by a grant(code 2023-006)from Gyeonggi Technology Development Program funded by Gyeonggi Province,Republic of Koreasupported by the research grant of The University of Suwon,Republic of Korea in 2023(2023-0166).
文摘Coupled electrochemical systems for the direct capture and conversion of CO have garnered significant attention owing to their potential to enhance energy-and cost-efficiency by circumventing the amine regeneration step.However,optimizing the coupled system is more challenging than handling separated systems because of its complexity,caused by the incorporation of solvent and heterogeneous catalysts.Nevertheless,the deployment of machine learning can be immensely beneficial,reducing both time and cost owing to its ability to simulate and describe complex systems with numerous parameters involved.In this review,we summarized the machine learning techniques employed in the development of CO_(2)capture solvents such as amine and ionic liquids,as well as electrochemical CO_(2)conversion catalysts.To optimize a coupled electrochemical system,these two separately developed systems will need to be combined via machine learning techniques in the future.
基金supported by a grant from the National Research Foundation of Korea(NRF)funded by the Korean government,Ministry of Science and ICT(MSIT)(No.2021R1A6A3A01086766)。
文摘We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do.While the error rate is high,generative AI seems to be able to effectively structure vast amounts of scientific knowledge and provide interesting and testable hypotheses.The future scientific enterprise may include synergistic efforts with a swarm of“hypothesis machines”,challenged by automated experimentation and adversarial peer reviews.
