Fortnet Recipes

• Introduction
  • Before You Start
  • Where to start
• Basic Usage
  • First Training with Fortnet
  • First Predictions with Fortnet
  • Incorporating External Atomic Features
• Fortformat
  • Fnetdata: Generating a Dataset
  • Fnetout: Extracting the Output
  • Fortformat: Basic Fortnet IO Format Classes
• Behler-Parrinello-Neural-Network
• Atom-Centered Symmetry Functions
  • Cross-Functional Settings
  • Manual G-Function Specification
  • Automatic Parameter Generation
  • Atom-Specific Scaling Factors
• Activation Functions
  • Hyperbolic Tangent
  • Arcus Tangent
  • Sigmoid
  • SoftPlus
  • Gaussian
  • ReLU
  • Leaky ReLU
  • Bent Identity
  • Heaviside
  • Linear
• Loss Functions
  • Mean Squared Error
  • Root Mean Square Error
  • Mean Absolute Error
  • Mean Absolute Percentage Error
  • Regularization
• Optimizer
  • General Optimizer Settings
  • Optimizer Specific Settings
• Analysis
  • Calculating First Derivatives
• Licence
• Bibliography