The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explore...The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.展开更多
Bipolar disorder(BD)is a severe mood disorder characterized by recurrent episodes of mania and depression,and it is prone to delayed diagnosis,which can lead to worsened outcomes,including more frequent mood episodes,...Bipolar disorder(BD)is a severe mood disorder characterized by recurrent episodes of mania and depression,and it is prone to delayed diagnosis,which can lead to worsened outcomes,including more frequent mood episodes,greater functional impairment,and comorbidities.Early diagnosis of BD remains a significant challenge,although recent advances offer promising insights,such as research in molecular biomarkers,neuroimaging,exosomes,genetics,and epigenetics.This mini-review highlights their potential for providing earlier,more accurate identification of BD and discusses the underlying reasons why current research has not yet succeeded.For instance,the high heterogeneity of symptomatic presentations leads to low consistency in study participants;delayed BD diagnosis results in the inclusion of potential BD patients in the depression group;low specificity of biomarkers stems from limited understanding of BD pathophysiology;and there is a possibility that BD is not innate but develops over the course of the disease.Deepening our understanding of BD pathology,identifying more specific biomarkers,and integrating multiomics approaches for validation studies in well-defined homogeneous cohorts hold promise for significant breakthroughs.展开更多
基金supported by the Special Project of National Natural Science Foundation(42341204)the the National Natural Science Foundation of China(W2411009).
文摘The integration of artificial intelligence (AI) with high-throughput experimentation (HTE) techniques is revolutionizing catalyst design, addressing challenges in efficiency, cost, and scalability. This review explores the synergistic application of AI and HTE, highlighting their role in accelerating catalyst discovery, optimizing reaction parameters, and understanding structure-performance relationships. HTE facilitates the rapid preparation, characterization, and evaluation of diverse catalyst formulations, generating large datasets essential for AI model training. Machine learning algorithms, including regression models, neural networks, and active learning frameworks, analyze these datasets to uncover the underlying relationships between the data, predict performance, and optimize experimental workflows in real-time. Case studies across heterogeneous, homogeneous, and electrocatalysis demonstrate significant advancements, including improved reaction selectivity, enhanced material stability, and shorten discovery cycles. The integration of AI with HTE has significantly accelerated discovery cycles, enabling the optimization of catalyst formulations and reaction conditions. Despite these achievements, challenges remain, including reliance on researcher expertise, real-time adaptability, and the complexity of large-scale data analysis. Addressing these limitations through refined experimental protocols, standardized datasets, and interpretable AI models will unlock the full potential of AI-HTE integration.
基金Supported by Research Plan Project of Tianjin Municipal Education Commission,No.2022KJ264.
文摘Bipolar disorder(BD)is a severe mood disorder characterized by recurrent episodes of mania and depression,and it is prone to delayed diagnosis,which can lead to worsened outcomes,including more frequent mood episodes,greater functional impairment,and comorbidities.Early diagnosis of BD remains a significant challenge,although recent advances offer promising insights,such as research in molecular biomarkers,neuroimaging,exosomes,genetics,and epigenetics.This mini-review highlights their potential for providing earlier,more accurate identification of BD and discusses the underlying reasons why current research has not yet succeeded.For instance,the high heterogeneity of symptomatic presentations leads to low consistency in study participants;delayed BD diagnosis results in the inclusion of potential BD patients in the depression group;low specificity of biomarkers stems from limited understanding of BD pathophysiology;and there is a possibility that BD is not innate but develops over the course of the disease.Deepening our understanding of BD pathology,identifying more specific biomarkers,and integrating multiomics approaches for validation studies in well-defined homogeneous cohorts hold promise for significant breakthroughs.
