Towards the SMART workflow system for computational spectroscopy
Author:
Affiliation:
1. Scuola Normale Superiore
2. 56126 Pisa
3. Italy
4. Istituto Italiano di Tecnologia
5. 16163 Genova
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?
Funder
Ministero dell’Istruzione, dell’Università e della Ricerca
Scuola Normale Superiore
Publisher
Royal Society of Chemistry (RSC)
Subject
Physical and Theoretical Chemistry,General Physics and Astronomy
Link
http://pubs.rsc.org/en/content/articlepdf/2018/CP/C8CP03417F
Reference166 articles.
1. Cyberinfrastructure for e-Science
2. T. Hey , S.Tansley and K.Tolle , The fourth paradigm: data-intensive scientific discovery , Microsoft Research , Redmond, Washington , 2009
3. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
4. Machine learning for quantum mechanics in a nutshell
5. Deep learning for computational chemistry
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