Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN).
Prathik R. KaundinyaKamal ChoudharySurya R. KalidindiPublished in: CoRR (2022)
Keyphrases
- neural network
- prediction accuracy
- prediction model
- artificial neural networks to predict
- pattern recognition
- graph theory
- random walk
- prediction algorithm
- neural network ensemble
- experimental data
- multi layer perceptron
- graph matching
- chemical compounds
- neural networks and support vector machines
- artificial neural networks
- graph databases
- directed graph
- structured data
- recurrent neural networks
- back propagation
- graph structure
- weighted graph
- multi layer
- graph model
- drug discovery
- initial state
- neural nets
- graph partitioning
- prediction error
- multilayer perceptron
- line segments
- self organizing maps
- fuzzy logic
- genetic algorithm