Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties.
Gianmarco TerronesChenru DuanAditya NandyHeather J. KulikPublished in: CoRR (2022)
Keyphrases
- low cost
- machine learning
- prediction accuracy
- support vector machine
- computational intelligence
- data sets
- low power
- decision trees
- pattern recognition
- natural language
- state transition
- transition model
- computer vision
- linear prediction
- machine learning approaches
- structural properties
- inductive logic programming
- machine learning methods
- machine learning algorithms
- knowledge acquisition
- state space
- knowledge representation
- artificial neural networks
- case study