Skip to main content

Unfortunately we don't fully support your browser. If you have the option to, please upgrade to a newer version or use Mozilla Firefox, Microsoft Edge, Google Chrome, or Safari 14 or newer. If you are unable to, and need support, please send us your feedback.

Elsevier
Publish with us

Download "How Data Science is Changing R&D in Chemicals and Materials"

This collection of published research from Elsevier Innovation Intelligence provides an in-depth look at how industry innovators are using data science to propel R&D.

Download now to access the following articles:

Natural language processing-guided meta-analysis and structure factor database extraction from glass literature. Journal of Non-Crystalline Solids: X, Volume 15, September 2022. Mohd Zaki, Sahith Reddy Namireddy, Tanu Pittie, Vaibhav Bihani, Shweta Rani Keshri, Vineeth Venugopal, Nitya Nand Gosvami, Jayadeva, N.M. Anoop Krishnan.

History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining. Solar Energy, Volume 169, 1 July 2018. Dazhi Yang, Jan Kleissl, Christian A. Gueymard, Hugo T.C. Pedro, Carlos F.M. Coimbra.

Machine learning models representing catalytic activity for direct catalytic CO2 hydrogenation to methanol. Materials Today: Proceedings, Volume 72, Part 1, 2023. Pallavi Vanjari, Reddi Kamesh, K. Yamuna Rani.

About you
About your organization