Official Resources
- Source Repository: https://github.com/tnorthey/mlxanes
- License: Open source
Overview
MLXANES is an OpenMP-parallelized multivariate linear regression Fortran program for predicting X-ray Absorption Near Edge Structure (XANES) spectra from atomic structure (XYZ files). It uses machine learning to establish structure-spectrum relationships, enabling rapid XANES prediction without first-principles calculations.
Scientific domain: ML XANES prediction, structure-spectrum mapping
Target user community: Researchers needing fast XANES prediction from atomic structure for screening and analysis
Theoretical Methods
- Multivariate linear regression
- Structure descriptors (Coulomb matrix, symmetry functions)
- XANES spectral prediction
- Machine learning regression
- OpenMP parallelization
Capabilities (CRITICAL)
- XANES prediction from XYZ structure
- Multivariate linear regression model
- OpenMP parallel training
- Structure-to-spectrum mapping
- Rapid spectral prediction
- Training on FEFF or DFT data
Sources: GitHub repository
Key Strengths
Speed:
- Millisecond prediction after training
- OpenMP parallel training
- Fortran performance
- Suitable for large datasets
Structure-Based:
- Direct XYZ input
- No DFT calculation needed for prediction
- Simple descriptor model
- Interpretable regression
Fortran Implementation:
- High performance
- OpenMP parallelization
- Efficient memory usage
- Production-quality code
Inputs & Outputs
-
Input formats:
- XYZ structure files
- Training XANES data
- Model parameters
-
Output data types:
- Predicted XANES spectra
- Regression coefficients
- Training statistics
- Prediction confidence
Interfaces & Ecosystem
- FEFF/DFT: Training data generation
- XYZ files: Standard structure format
- OpenMP: Parallel execution
Performance Characteristics
- Speed: Very fast prediction (milliseconds)
- Accuracy: Moderate (linear regression)
- System size: Limited by descriptor
- Memory: Low
Computational Cost
- Prediction: Milliseconds
- Training: Minutes to hours (parallel)
- Typical: Very efficient
Limitations & Known Constraints
- Linear regression: Limited model capacity
- Descriptor dependent: Quality depends on descriptor choice
- Extrapolation: Poor outside training domain
- Fortran: Less accessible than Python
- Documentation: Very limited
Comparison with Other Codes
- vs XANESNET: MLXANES uses linear regression, XANESNET uses DNN (more accurate but slower)
- vs pyFitIt: MLXANES is forward prediction, pyFitIt is inverse fitting
- vs FEFF: MLXANES is much faster, FEFF is more general and accurate
- Unique strength: Fast Fortran ML XANES prediction from XYZ structure, OpenMP parallel
Application Areas
Rapid XANES Screening:
- Materials databases
- Structure validation
- Quick spectral assessment
- Preliminary analysis
Structure-Spectrum Correlation:
- Understanding XANES features
- Descriptor importance analysis
- Feature-spectrum relationships
- Training set design
Best Practices
Training Data:
- Use diverse structural motifs
- Validate on test set
- Monitor regression quality
- Use sufficient training points
Prediction:
- Check input is within training domain
- Compare with FEFF for validation
- Use appropriate descriptors
- Report prediction uncertainty
Community and Support
- Open source on GitHub
- Research code
- Limited documentation
- Fortran-based
Verification & Sources
Primary sources:
- GitHub repository: https://github.com/tnorthey/mlxanes
- T. Northey et al., related publications
Confidence: VERIFIED
Verification status: ✅ VERIFIED
- Source code: ACCESSIBLE (GitHub)
- Documentation: Limited
- Active development: Research code
- Specialized strength: Fast Fortran ML XANES prediction from atomic structure, OpenMP parallel