AlphaCrystal

AlphaCrystal is a contact map-based deep learning algorithm for crystal structure prediction, inspired by AlphaFold's approach to protein structure prediction. AlphaCrystal-II extends this with distance matrix-based prediction.

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Overview

AlphaCrystal is a contact map-based deep learning algorithm for crystal structure prediction, inspired by AlphaFold's approach to protein structure prediction. AlphaCrystal-II extends this with distance matrix-based prediction.

Reference Papers

Reference papers are not yet linked for this code.

Full Documentation

Overview

AlphaCrystal is a contact map-based deep learning algorithm for crystal structure prediction, inspired by AlphaFold's approach to protein structure prediction. AlphaCrystal-II extends this with distance matrix-based prediction.

Theoretical Basis

  • Contact map prediction
  • Distance matrix prediction (AlphaCrystal-II)
  • Deep residual neural networks
  • Structure reconstruction from distances
  • Composition-to-structure mapping

Key Capabilities

  • Crystal structure prediction from composition
  • Contact/distance map prediction
  • Deep learning approach
  • No DFT during prediction
  • Fast inference

Sources: ACS Omega (2023), arXiv:2404.04810

Key Strengths

Methodology:

  • AlphaFold-inspired
  • Contact/distance maps
  • Deep learning

Speed:

  • Fast inference
  • No DFT required
  • Large-scale applicable

Innovation:

  • Novel approach
  • Protein-inspired
  • Distance-based

Inputs & Outputs

  • Input formats: Chemical composition
  • Output data types: Predicted crystal structures, distance maps

Interfaces & Ecosystem

  • Framework: TensorFlow/PyTorch
  • Tools: MLatticeABC, Cryspnet
  • Validation: DFT codes

Workflow and Usage

  1. Input chemical composition
  2. Predict distance/contact map
  3. Reconstruct 3D structure
  4. Refine structure
  5. Validate with DFT

Performance Characteristics

  • Fast prediction
  • GPU-accelerated
  • Good for screening

Computational Cost

  • Prediction: fast
  • No DFT required
  • Validation: DFT

Best Practices

  • Use for initial screening
  • Validate top predictions
  • Consider multiple candidates
  • Check structural validity

Limitations & Known Constraints

  • Training data dependent
  • May miss novel structures
  • Requires validation

Application Areas

  • High-throughput screening
  • Initial structure guessing
  • Materials discovery
  • Composition-to-structure

Comparison with Other Codes

  • vs DiffCSP: Different approach (maps vs diffusion)
  • vs CSPML: Different methodology
  • Unique strength: AlphaFold-inspired, distance maps

Community and Support

  • Open-source (GitHub)
  • USC development
  • Published methodology

Verification & Sources

Primary sources:

  1. GitHub: https://github.com/usccolumbia/AlphaCrystal
  2. AlphaCrystal: ACS Omega (2023)
  3. AlphaCrystal-II: arXiv:2404.04810

Confidence: VERIFIED

Verification status: ✅ VERIFIED

  • Website: ACTIVE (GitHub)
  • Source: OPEN
  • Development: ACTIVE
  • Applications: Crystal structure prediction

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