Data Science in Materials Research
- (The University of Chicago - Alvin Wei-Cheng Wong)
- Overview
Data science in materials research is a scientific practice that involves systematically extracting knowledge from materials datasets. It combines computer science, math, physics, chemistry, and materials science to explore the nature of production and phenomena.
Data science in materials research can include:
- Predictive maintenance: Using data science to discover new or alternative materials
- Computational tools: Using large amounts of data to help with decisions about materials selection and design
- Diagnostic tools: Building tools to prove the reliability of existing stockpiles
- Time-series analyses: Conducting analyses of power plant performance to evaluate energy efficiency
Data science in materials research can also include:
- Machine learning
- Statistical and mathematical methods
- Supervised learning
- techniques for regression
- and classification
[More to come ...]
