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The use of generative models to speed up the discovery of materials
Andrea Gregores Coto
, Christian Eike Precker
*
,
Tom Andersson
,
Anssi Laukkanen
,
Tomi Suhonen
, Pilar Rey Rodriguez
, Santiago Muíños-Landín
*
*
Corresponding author for this work
BA5502 ProperTune ICME
BA55 Knowledge driven design
Asociación De Investigación Metalúrgica Del Noroeste (AIMEN)
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INIS
performance
100%
design
100%
polymers
100%
speed
100%
ceramics
100%
alloys
100%
entropy
100%
data
75%
industry
50%
artificial intelligence
50%
simulation
25%
economics
25%
sustainability
25%
comparative evaluations
25%
resources
25%
construction
25%
energy
25%
phase diagrams
25%
industrial sector
25%
synthesis
25%
datasets
25%
verification
25%
biotechnology
25%
impulse
25%
Keyphrases
Generative Models
100%
Polymer-ceramic Composites
100%
High-entropy Alloy
100%
Material Design
50%
High-performance Materials
50%
Environmental Sustainability
25%
Economic Sustainability
25%
Industrial Processes
25%
Industrial Sector
25%
High Performance
25%
Synthetic Data
25%
Design Parameters
25%
Automotive Industry
25%
Construction Industry
25%
Design Phase
25%
Artificial Intelligence
25%
Efficient Use of Energy
25%
New Functionalities
25%
Whole Process
25%
Materials Science
25%
Artificial Intelligence Techniques
25%
Synthetic Compounds
25%
Generated Data
25%
Generated Samples
25%
Biotechnology Industry
25%
Calculation of Phase Diagrams (CALPHAD)
25%
Materials Database
25%
Tabular Data
25%
Aeronautical Industry
25%
Open Materials
25%
Synthesizability
25%
Parametric Design
25%
Phase Diagram Simulation
25%
Emergent Strategy
25%
Conditional Generative Adversarial Network (cGAN)
25%
Element Combination
25%
Engineering
Generative Model
100%
High-Entropy Alloys
100%
Material Performance
50%
Industrial Sector
25%
Design Parameter
25%
Automotives
25%
Artificial Intelligence Technique
25%
Initial Phase
25%
Generated Sample
25%
Material Database
25%
Design Phase
25%
Artificial Intelligence
25%
Material Science
High Entropy Alloys
100%
Materials Design
25%