Is it possible to convert highly specialized research in the field of computational spectroscopy into robust and user-friendly aids to experiments and industrial applications? What kind of tools should be created to increase the interactions between researchers with different backgrounds and push towards new frontiers in computational chemistry? The outstanding advances in computational spectroscopy and the wide availability of computational and analytical tools are paving the route toward the study of problems that were previously difficult or impossible to solve and enable the imagination of even more ambitious targets for fundamental and applied research. The combination of new computer- and data-centric technologies is transforming data analysis from an uncommon and retrospective practice into a proactive process of strategic decision and action. This paper starts from these premises and proposes a perspective for a new cyberinfrastructure aimed at integrating developments in theory, algorithms and software with new tools for workflow management, data mining and visualization. We make a case for this approach by means of a few examples that deal with unmanageable types of data in molecular modelling and results obtained using different unsupervised learning algorithms.

Is it possible to convert highly specialized research in the field of computational spectroscopy into robust and user-friendly aids to experiments and industrial applications? What kind of tools should be created to increase the interactions between researchers with different backgrounds and push towards new frontiers in computational chemistry? The outstanding advances in computational spectroscopy and the wide availability of computational and analytical tools are paving the route toward the study of problems that were previously difficult or impossible to solve and enable the imagination of even more ambitious targets for fundamental and applied research. The combination of new computer- and data-centric technologies is transforming data analysis from an uncommon and retrospective practice into a proactive process of strategic decision and action. This paper starts from these premises and proposes a perspective for a new cyberinfrastructure aimed at integrating developments in theory, algorithms and software with new tools for workflow management, data mining and visualization. We make a case for this approach by means of a few examples that deal with unmanageable types of data in molecular modelling and results obtained using different unsupervised learning algorithms.

Towards the SMART workflow system for computational spectroscopy

D. Licari;M. Fusè;A. Salvadori;N. Tasinato;M. Mendolicchio;G. Mancini;V. Barone
2018

Abstract

Is it possible to convert highly specialized research in the field of computational spectroscopy into robust and user-friendly aids to experiments and industrial applications? What kind of tools should be created to increase the interactions between researchers with different backgrounds and push towards new frontiers in computational chemistry? The outstanding advances in computational spectroscopy and the wide availability of computational and analytical tools are paving the route toward the study of problems that were previously difficult or impossible to solve and enable the imagination of even more ambitious targets for fundamental and applied research. The combination of new computer- and data-centric technologies is transforming data analysis from an uncommon and retrospective practice into a proactive process of strategic decision and action. This paper starts from these premises and proposes a perspective for a new cyberinfrastructure aimed at integrating developments in theory, algorithms and software with new tools for workflow management, data mining and visualization. We make a case for this approach by means of a few examples that deal with unmanageable types of data in molecular modelling and results obtained using different unsupervised learning algorithms.
Settore CHIM/12 - Chimica dell'Ambiente e dei Beni Culturali
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11384/75924
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