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Machine learning and data science in soft materials engineering
Andrew L. Ferguson
National Center for Supercomputing Applications (NCSA)
Physics
Materials Research Lab
Materials Science and Engineering
Chemical and Biomolecular Engineering
Beckman Institute for Advanced Science and Technology
Research output
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peer-review
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Keyphrases
Science Learning
100%
Soft Materials
100%
Material Engineering
100%
Machine Learning
100%
Data Science
100%
Machine Data
100%
High-throughput
33%
Popular
33%
Materials Science
33%
Biological Materials
33%
Illustrative Examples
33%
Principal Coordinate Analysis (PCoA)
33%
Inverse Design
33%
Phase Space
33%
Material Nonlinearity
33%
Software Implementation
33%
Self-assembled Materials
33%
Antimicrobial Peptides
33%
Traversal
33%
Relative Entropy
33%
Machine Learning Techniques
33%
Scalable Approaches
33%
Soft Matter
33%
Machine Learning Applications
33%
Support Vector Machine
33%
International Cooperative Ataxia Rating Scale (ICARS)
33%
Diffusion Maps
33%
Protein Folding Landscape
33%
Machine Learning System
33%
Data Strategy
33%
Nonlinear Learning
33%
Peptide Design
33%
Data-driven Materials Design
33%
Inverse Material Design
33%
Science Terminology
33%
Voluminous Data
33%
Design Engine
33%
Material Science
Materials Design
100%
Materials Science
100%
Biological Material
50%