Intelligent algorithms for machine learning, as well as “big data,” are seen as calculation and support tools for chemical applications, but never as an objective in and of themselves. The improvements and recent growth that the use of these tools has seen in the past few years can cover fields that are directly or indirectly related to chemistry, such as health or food technology, or even be implemented to aid in the development of pharmaceutical products or industrial applications. As such, it is timely to create a Research Topic where advances in this field, linked directly to chemistry, are presented.
On many occasions, due the great amount of information contained within databases arising from research and industry, the only way to tackle them in order to achieve usable and implementable applications is through machine learning. Machine learning can ensure that all useful hidden information contained in databases is interpreted effectively, and utilized to its fullest potential.
Intelligent or cognitive modeling enables the creation of tools that can be easily implemented into analytical equipment to serve as final detecting, quantifying, or classifying tools. With the capability of integrating optimized software to carry out many tasks, these algorithms can be coupled with data generating systems and directly provide outputs for many purposes in the chemical fields. On the other hand, the intrinsic nature of computational artificial intelligence allows for the ability to update and refresh the models as new data is generated, leading to more robust tools that cover larger windows of operation and eliminate negative confounding factors.
In this Research Topic, cutting-edge applications where computational artificial intelligence is employed to analyze chemical processes are welcome. Specifically, tools based on artificial neural networks and all of their supervised and non-supervised options, Bayesian networks, genetic algorithms, chaotic parameters, random forests, adaptive systems, expert systems, data mining, and so on, would be well-suited. The applications can be linked to modeling and/or optimizing chemical processes in any of its disciplines (organic chemistry, inorganic chemistry, materials sciences, chemical engineering, biochemistry, etc.), designing chemometric tools, estimating physicochemical properties, resolving complex mixtures, designing intelligent sensors, and many more potential applications.
Only contributions dealing with core chemistry will be considered for this collection; applications in the aforementioned fields must be related to modeling a clearly chemical phenomenon or process.
Intelligent algorithms for machine learning, as well as “big data,” are seen as calculation and support tools for chemical applications, but never as an objective in and of themselves. The improvements and recent growth that the use of these tools has seen in the past few years can cover fields that are directly or indirectly related to chemistry, such as health or food technology, or even be implemented to aid in the development of pharmaceutical products or industrial applications. As such, it is timely to create a Research Topic where advances in this field, linked directly to chemistry, are presented.
On many occasions, due the great amount of information contained within databases arising from research and industry, the only way to tackle them in order to achieve usable and implementable applications is through machine learning. Machine learning can ensure that all useful hidden information contained in databases is interpreted effectively, and utilized to its fullest potential.
Intelligent or cognitive modeling enables the creation of tools that can be easily implemented into analytical equipment to serve as final detecting, quantifying, or classifying tools. With the capability of integrating optimized software to carry out many tasks, these algorithms can be coupled with data generating systems and directly provide outputs for many purposes in the chemical fields. On the other hand, the intrinsic nature of computational artificial intelligence allows for the ability to update and refresh the models as new data is generated, leading to more robust tools that cover larger windows of operation and eliminate negative confounding factors.
In this Research Topic, cutting-edge applications where computational artificial intelligence is employed to analyze chemical processes are welcome. Specifically, tools based on artificial neural networks and all of their supervised and non-supervised options, Bayesian networks, genetic algorithms, chaotic parameters, random forests, adaptive systems, expert systems, data mining, and so on, would be well-suited. The applications can be linked to modeling and/or optimizing chemical processes in any of its disciplines (organic chemistry, inorganic chemistry, materials sciences, chemical engineering, biochemistry, etc.), designing chemometric tools, estimating physicochemical properties, resolving complex mixtures, designing intelligent sensors, and many more potential applications.
Only contributions dealing with core chemistry will be considered for this collection; applications in the aforementioned fields must be related to modeling a clearly chemical phenomenon or process.
