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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
Overview
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Keyphrases
Antimicrobial Peptides
33%
Biological Materials
33%
Data Science
100%
Data Strategy
33%
Data-driven Materials Design
33%
Design Engine
33%
Diffusion Maps
33%
High-throughput
33%
Illustrative Examples
33%
International Cooperative Ataxia Rating Scale (ICARS)
33%
Inverse Design
33%
Inverse Material Design
33%
Machine Data
100%
Machine Learning
100%
Machine Learning Applications
33%
Machine Learning System
33%
Machine Learning Techniques
33%
Material Engineering
100%
Material Nonlinearity
33%
Materials Science
33%
Nonlinear Learning
33%
Peptide Design
33%
Phase Space
33%
Popular
33%
Principal Coordinate Analysis (PCoA)
33%
Protein Folding Landscape
33%
Relative Entropy
33%
Scalable Approaches
33%
Science Learning
100%
Science Terminology
33%
Self-assembled Materials
33%
Soft Materials
100%
Soft Matter
33%
Software Implementation
33%
Support Vector Machine
33%
Traversal
33%
Voluminous Data
33%
Material Science
Antimicrobial Peptide
50%
Biomaterial
50%
Materials Design
100%
Materials Engineering
100%
Protein Folding
50%