The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Leroy Cronin - One of the best experts on this subject based on the ideXlab platform.

  • intuition enabled machine learning beats the competition when joint human robot teams perform Inorganic Chemical experiments
    Journal of Chemical Information and Modeling, 2019
    Co-Authors: Vasilios Duros, Jonathan Grizou, Abhishek Sharma, Andrius Bubliauskas, Haralampos N. Miras, Hessam S. M. Mehr, Przemyslaw Frei, Leroy Cronin
    Abstract:

    Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving toward the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces, as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the Chemical space by comparing the collaboration between human experimenters with an algorithm-based search against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone and 66.3 ± 1.8% from only the human experimenters demonstrating that human-robot teams can beat robots or humans working alone.

  • Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments
    2019
    Co-Authors: Vasilios Duros, Jonathan Grizou, Abhishek Sharma, Andrius Bubliauskas, Przemysław Frei, Haralampos N. Miras, Hessam S. M. Mehr, Leroy Cronin
    Abstract:

    Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving toward the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces, as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the Chemical space by comparing the collaboration between human experimenters with an algorithm-based search against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone and 66.3 ± 1.8% from only the human experimenters demonstrating that human-robot teams can beat robots or humans working alone

  • Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments
    2019
    Co-Authors: Leroy Cronin, Vasilios Duros, Jonathan Grizou, Abhishek Sharma, Hessam Mehr, Andrius Bubliauskas, Przemysław Frei, Haralampos N. Miras
    Abstract:

    Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules so vast, only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving towards the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the Chemical space by comparing collaboration between human experimenters with an algorithm-based search, against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6±1.8%, from 71.8±0.3% with the algorithm alone and 66.3±1.8% from only the human experimenters demonstrating that human-robot teams beat robots or humans working alone.

  • modular redox active Inorganic Chemical cells ichells
    Angewandte Chemie, 2011
    Co-Authors: Geoffrey J T Cooper, Philip J Kitson, Ross S Winter, Michele Zagnoni, Deliang Long, Leroy Cronin
    Abstract:

    Interfacial membrane formation by cation exchange of polyoxometalates produces modular Inorganic Chemical cells with tunable morphology, properties, and composition (see picture). These Inorganic Chemical cells (iCHELLs), which show redox activity, chirality, as well as selective permeability towards small molecules, can be nested within one another, potentially allowing stepwise reactions to occur in sequence within the cell.

Liangbao Yang - One of the best experts on this subject based on the ideXlab platform.

  • monitoring the Inorganic Chemical reaction by surface enhanced raman spectroscopy a case of fe3 to fe2 conversion
    Talanta, 2016
    Co-Authors: Juan Meng, Xianghu Tang, Liangbao Yang
    Abstract:

    Abstract Monitoring the process of organic Chemical reactions to study the kinetics by surface-enhanced Raman spectroscopy (SERS) is currently of immense interest. However, monitoring the Inorganic Chemical reaction is still an extremely difficulty for researchers. This study exactly focused on the monitor of Inorganic Chemical reaction. Capillary coated with silver nanoparticles was introduced, which was an efficient platform for monitoring reactions with SERS due to the advantages of sensitivity and excellent reproducibility. The photoreduction of [Fe(phen) 3 ] 3+ to [Fe(phen) 3 ] 2+ was used as model reaction to demonstrated the feasibility of SERS monitoring Inorganic Chemical reaction by involving in metal–organic complexes. Moreover, the preliminary implementation demonstrated that the kinetics of photoreduction can be real-time monitored by in situ using the SERS technique on a single constructed capillary, which may be useful for the practical application of SERS technique.

Haider Iqbal Khan - One of the best experts on this subject based on the ideXlab platform.

  • appraisal of biofertilizers in rice to supplement Inorganic Chemical fertilizer
    Rice Science, 2018
    Co-Authors: Haider Iqbal Khan
    Abstract:

    Abstract A field experiment was carried out to evaluate the feasibility of inoculating rice seedlings with biofertilizers ( Azospirillum and Trichoderma ) in order to reduce the use of Chemical Inorganic nitrogen (N) fertilizer on rice variety BU Dhan 1. The plant performances were better when 25% less Inorganic N was applied with Trichoderma and combined application of Trichoderma and Azospirillum . Plants contained the highest chlorophyll concentrations when they were treated with 75% N +  Trichoderma . Considering the yield attributes, 75% N +  Trichoderma and 75% N +  Trichoderma  +  Azospirillum performed similar to the control. The grain yield of rice was similar to the recommended dose even with 25% less N application. Application of Trichoderma resulted higher yield, followed by combined application with Azospirillum . Results revealed the greater scope of applying biofertilizer ( Trichoderma ) to supplement Chemical N fertilizer with optimum yield of rice.

Juan Meng - One of the best experts on this subject based on the ideXlab platform.

  • monitoring the Inorganic Chemical reaction by surface enhanced raman spectroscopy a case of fe3 to fe2 conversion
    Talanta, 2016
    Co-Authors: Juan Meng, Xianghu Tang, Liangbao Yang
    Abstract:

    Abstract Monitoring the process of organic Chemical reactions to study the kinetics by surface-enhanced Raman spectroscopy (SERS) is currently of immense interest. However, monitoring the Inorganic Chemical reaction is still an extremely difficulty for researchers. This study exactly focused on the monitor of Inorganic Chemical reaction. Capillary coated with silver nanoparticles was introduced, which was an efficient platform for monitoring reactions with SERS due to the advantages of sensitivity and excellent reproducibility. The photoreduction of [Fe(phen) 3 ] 3+ to [Fe(phen) 3 ] 2+ was used as model reaction to demonstrated the feasibility of SERS monitoring Inorganic Chemical reaction by involving in metal–organic complexes. Moreover, the preliminary implementation demonstrated that the kinetics of photoreduction can be real-time monitored by in situ using the SERS technique on a single constructed capillary, which may be useful for the practical application of SERS technique.

Vasilios Duros - One of the best experts on this subject based on the ideXlab platform.

  • intuition enabled machine learning beats the competition when joint human robot teams perform Inorganic Chemical experiments
    Journal of Chemical Information and Modeling, 2019
    Co-Authors: Vasilios Duros, Jonathan Grizou, Abhishek Sharma, Andrius Bubliauskas, Haralampos N. Miras, Hessam S. M. Mehr, Przemyslaw Frei, Leroy Cronin
    Abstract:

    Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving toward the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces, as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the Chemical space by comparing the collaboration between human experimenters with an algorithm-based search against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone and 66.3 ± 1.8% from only the human experimenters demonstrating that human-robot teams can beat robots or humans working alone.

  • Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments
    2019
    Co-Authors: Leroy Cronin, Vasilios Duros, Jonathan Grizou, Abhishek Sharma, Hessam Mehr, Andrius Bubliauskas, Przemysław Frei, Haralampos N. Miras
    Abstract:

    Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules so vast, only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving towards the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the Chemical space by comparing collaboration between human experimenters with an algorithm-based search, against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6±1.8%, from 71.8±0.3% with the algorithm alone and 66.3±1.8% from only the human experimenters demonstrating that human-robot teams beat robots or humans working alone.

  • Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments
    2019
    Co-Authors: Vasilios Duros, Jonathan Grizou, Abhishek Sharma, Andrius Bubliauskas, Przemysław Frei, Haralampos N. Miras, Hessam S. M. Mehr, Leroy Cronin
    Abstract:

    Traditionally, chemists have relied on years of training and accumulated experience in order to discover new molecules. But the space of possible molecules is so vast that only a limited exploration with the traditional methods can be ever possible. This means that many opportunities for the discovery of interesting phenomena have been missed, and in addition, the inherent variability of these phenomena can make them difficult to control and understand. The current state-of-the-art is moving toward the development of automated and eventually fully autonomous systems coupled with in-line analytics and decision-making algorithms. Yet even these, despite the substantial progress achieved recently, still cannot easily tackle large combinatorial spaces, as they are limited by the lack of high-quality data. Herein, we explore the utility of active learning methods for exploring the Chemical space by comparing the collaboration between human experimenters with an algorithm-based search against their performance individually to probe the self-assembly and crystallization of the polyoxometalate cluster Na6[Mo120Ce6O366H12(H2O)78]·200H2O (1). We show that the robot-human teams are able to increase the prediction accuracy to 75.6 ± 1.8%, from 71.8 ± 0.3% with the algorithm alone and 66.3 ± 1.8% from only the human experimenters demonstrating that human-robot teams can beat robots or humans working alone