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 analyze 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 analyze 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 datasets – and can easily share them with their colleagues to ensure best practices are conserved across a research group.

Join this webinar to discover:

• Use cases showing how machine learning and Big Data analytics can help drive more confident data-driven decisions
• How data science pipelining tools can help make these techniques more accessible
• How these tools simplify the model design process and outline approaches for differently sized data sets.

Company: 
Feature

As storage technology adapts to changing HPC workloads, Robert Roe looks at the technologies that could help to enhance performance and accessibility of
storage in HPC