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Leveraging Machine Learning for Decision Making in the Materials Sciences
Machine learning and big data analytics offer significant opportunities to improve R&D in the materials sciences, providing scientists with a new set of tools to analyse their data. These Machine learning and big data analytics offer significant opportunities to improve R&D in the materials sciences, providing scientists with a new set of tools to analyse their data. These approaches can help scientists do more with less, building a stronger, data-driven foundation for decision making and guiding future research. However, traditional methods to apply these techniques required extensive custom coding, deep technical knowledge, and prohibitively large data sets to create an effective model.
Recent advances in machine learning and big data analytics have helped to mitigate these requirements, with tools designed specifically for chemistry-focused data science. With these, scientists can develop custom models and algorithms much faster than before – no matter the size of their data sets – and can easily share them with their colleagues to ensure best practices are conserved across a research group.
Featured speaker Sean McGee Product Marketing Manager, BIOVIA
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Laboratory 4.0: Moving Beyond Digitalization in the Lab
Today’s laboratories are becoming increasingly complex, with ever more data being generated and captured. At the same time, regulatory oversight is stronger than ever and places new compliance burdens on everyday operations. In response to this landscape, many laboratories have already implemented digitalisation technologies or are in the process of doing so. But with the rise of Industry 4.0, which brings increased automation and digital information transfer in manufacturing, many organisations are contemplating what this means for the future of the lab.
Laboratory 4.0 brings these concepts into the lab, automating the capture and flow of digital data from all the disparate networked instruments and systems within the lab. The benefits are manifold, including more accurate results, reduced costs, greater efficiency, and improved collaboration. In this short webinar we will discuss how to evaluate the potential of a “Laboratory 4.0” approach leveraging today’s technology to implement various levels of automation in today’s research, development, and quality control labs.
Featured speaker Stephen Hayward Product Marketing Manager BIOVIA
SCIENTIFIC COMPUTING WORLD
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