Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning.
Logan T. WardBen BlaiszikCheng-Wei LeeTroy MartinIan T. FosterAndré SchleifePublished in: CoRR (2023)
Keyphrases
- machine learning
- learning algorithm
- data mining
- knowledge representation
- general theory
- inductive learning
- knowledge acquisition
- computer vision
- power consumption
- theoretical framework
- learning systems
- machine learning algorithms
- pattern recognition
- machine learning approaches
- theoretical basis
- travel time
- machine learning methods
- feature selection
- computer science
- reinforcement learning
- text classification
- computational intelligence
- natural language processing
- supervised learning
- decision trees
- active learning
- natural language
- combining multiple
- statistical learning theory
- artificial intelligence