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**GAP** (Gaussian Approximation Potential) with **QUIP** (Quantum Interatomic Potential) is the original MLIP framework using SOAP descriptors and sparse Gaussian processes. It pioneered the field of ML interatomic potentials and remains…

10. NICHE & ML 10.2 MLIPs - ACE/Linear VERIFIED 1 paper
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Overview

**GAP** (Gaussian Approximation Potential) with **QUIP** (Quantum Interatomic Potential) is the original MLIP framework using SOAP descriptors and sparse Gaussian processes. It pioneered the field of ML interatomic potentials and remains widely used for element-specific high-accuracy potentials.

Reference Papers (1)

Full Documentation

Official Resources

  • Source Repository: https://github.com/libAtoms/QUIP
  • Documentation: https://libatoms.github.io/QUIP/
  • License: Open source (GPL-2.0)

Overview

GAP (Gaussian Approximation Potential) with QUIP (Quantum Interatomic Potential) is the original MLIP framework using SOAP descriptors and sparse Gaussian processes. It pioneered the field of ML interatomic potentials and remains widely used for element-specific high-accuracy potentials.

Scientific domain: Gaussian process MLIP with SOAP descriptors
Target user community: Researchers fitting high-accuracy GP potentials for specific elements/systems

Theoretical Methods

  • Gaussian Approximation Potential (GAP)
  • SOAP (Smooth Overlap of Atomic Positions) descriptors
  • Sparse Gaussian process regression
  • 2-body, 3-body, and SOAP terms
  • Fortran core with Python interface (quippy)

Capabilities (CRITICAL)

  • SOAP-GAP fitting
  • Multi-body terms (2b, 3b, SOAP)
  • LAMMPS integration via quippy
  • ASE interface
  • Well-tested on many systems

Sources: GitHub repository, Int. J. Quantum Chem. 115, 1051 (2015)

Key Strengths

Pioneering:

  • First widely-used MLIP framework
  • SOAP descriptor standard
  • Well-validated on many systems
  • Published potentials library

Accuracy:

  • Near-DFT accuracy
  • Well-calibrated uncertainty
  • Systematic improvement
  • Physical constraints

Integration:

  • LAMMPS via quippy
  • ASE calculator
  • Fortran performance
  • Python flexibility

Inputs & Outputs

  • Input formats: Extended XYZ, VASP, CASTEP
  • Output data types: GAP potentials, energies, forces, virials

Interfaces & Ecosystem

  • LAMMPS: MD engine
  • ASE: Calculator (quippy)
  • Fortran: Core
  • Python: Interface

Performance Characteristics

  • Speed: Moderate (GP evaluation)
  • Accuracy: Near-DFT
  • System size: 100-10000 atoms
  • Automation: Semi-automated

Computational Cost

  • Fitting: Hours
  • MD: 10-100x faster than DFT

Limitations & Known Constraints

  • GPL license: Copyleft
  • Fortran build: Complex compilation
  • GP scaling: O(N²) training
  • Element-specific: Not universal out-of-box

Comparison with Other Codes

  • vs MACE: GAP is GP, MACE is NN
  • vs FLARE: GAP is batch, FLARE is on-the-fly
  • vs SNAP: GAP is GP, SNAP is linear
  • Unique strength: Pioneering MLIP framework with SOAP descriptors and sparse GP, well-validated across many systems

Application Areas

High-Accuracy Potentials:

  • Carbon (GAP-20)
  • Silicon, germanium
  • Tungsten, iron
  • Water, aqueous systems

Materials MD:

  • Phase transitions
  • Amorphous materials
  • Defect properties
  • Thermal transport

Best Practices

  • Use SOAP + 2b + 3b terms
  • Start from published potentials
  • Validate with phonons and elastic constants
  • Use quippy for LAMMPS integration

Community and Support

  • Open source (GPL-2.0)
  • libAtoms community
  • Cambridge/Oxford maintained
  • Comprehensive documentation

Verification & Sources

Primary sources:

  1. GitHub: https://github.com/libAtoms/QUIP

Confidence: VERIFIED

Verification status: ✅ VERIFIED

  • Source code: ACCESSIBLE (GitHub)
  • Specialized strength: Pioneering MLIP framework with SOAP descriptors and sparse GP

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