SPARC

Getting Started

  • SPARC Installation Guide
    • Core Software Dependencies
    • Python Package Dependencies
    • Step-by-Step Installation
    • DFT Engine Requirements
    • Environment Setup
    • Verification
  • Quick Start Guide
    • Set Environment Variables
    • Basic Usage
    • Example Input File
    • Running a Simulation
    • Directory Structure
    • Sample Output (Sparc.log)
    • Core Components
      • 1. MD Simulation
      • 2. MLIP Training
      • 3. Active Learning
  • Input File
    • General Settings
      • DFT Calculator
      • AB Initio MD (AIMD)
      • MLIP Setup
    • Restart Exploration
      • Fine-Tuning (Universal Models)
      • Active Learning
      • Distance Metrics
      • Output
      • Directory Structure
  • Tutorial
    • Ammonia Borate (NH\(_3\)BH\(_3\))
      • Introduction
      • Preparation
      • Execute SPARC
      • Training ML Models
      • Running ML/MD
      • Active Learning Loop
      • Monitor Workflow
        • [1] Workflow Analysis
        • [2] Free Energy Analysis
      • PLUMED Plugin
      • Next Step

User Guide

  • User Guide
    • Workflow Overview
      • Stage 1 — DFT / AIMD (00.dft)
      • Stage 2 — MLIP Training (01.train)
      • Stage 3 — ML-MD + Query-by-Committee (02.dpmd)
      • How sections in input.yaml map to stages
      • Loop termination
    • Active Learning
      • Force deviation thresholds
      • Number of models
      • Restarting an interrupted run
      • Restart exploration
    • Choosing a DFT Engine
      • VASP
      • CP2K
      • ORCA
      • xTB
      • Quantum ESPRESSO (QE)
      • Gaussian
    • Fine-Tuning vs. Training From Scratch
      • Training from scratch (default)
      • Fine-tuning a DeePMD universal model
      • When to use fine-tuning
    • Enhanced Sampling with PLUMED
      • Any PLUMED collective variable or bias can be used
        • Example: SPRINT coordinates
      • Umbrella sampling

Modules

  • DFT Calculator
    • Overview
    • Configuration (input.yaml)
    • Template File Reference
      • CP2K parameters (cp2k: section in input.yaml)
    • Module Contents
      • CalculatorError
      • SetupDFTCalculator
        • SetupDFTCalculator.input_config
        • SetupDFTCalculator.dft_config
        • SetupDFTCalculator.print_screen
        • SetupDFTCalculator.cp2k()
        • SetupDFTCalculator.espresso()
        • SetupDFTCalculator.gaussian()
        • SetupDFTCalculator.orca()
        • SetupDFTCalculator.vasp()
        • SetupDFTCalculator.xtb()
      • dft_calculator()
  • DeepMD Setup
    • Overview
    • Usage Example
    • Supported Backends
    • Module Contents
      • deepmd_training()
      • evaluate_model_accuracy()
      • get_backend()
      • get_version()
      • setup_DeepPotential()
      • update_json()
    • Graph Neural Network Potentials (GNN)
    • References
  • Fine-Tuning Universal Models
    • Overview
    • Configuration
    • DeePMD Fine-Tuning
    • Integration with Active Learning
    • Troubleshooting
    • Module Contents
      • deepmd_finetune()
      • finetune_training()
      • mace_finetune()
      • setup_MACE_calculator()
  • Molecular Dynamics
    • Overview
    • Features:
    • Usage Examples
    • Module Contents
      • CalculateDFTEnergy()
      • ExecuteAbInitioDynamics()
      • ExecuteMlpDynamics()
      • LangevinNVT()
      • NPT()
      • NVE()
      • NoseNVT()
      • TemperatureRamp()
      • initialize_dynamics()
      • lammps_md()
    • References
  • Data Processing
    • Overview
    • Usage
    • Train / Validation Split
    • Module Contents
      • get_data()
    • Reference
  • Plumed
    • Overview
    • SPRINT Coordinates
    • Enabling PLUMED in input.yaml
    • Umbrella Sampling
    • Module Contents
      • modify_forces()
      • umbrella()
  • Analysis
    • Overview
      • Model deviation in Forces
      • Function:
        • [1] Workflow Analysis
        • [2] Free Energy Analysis
      • RMSD Analysis
        • kabsch_rmsd()
  • Workflow
    • Function:
      • Features
      • Dependencies
      • Quick Start
      • Example Directory Structure
      • Geometry Tab Details
  • ChemView
    • ChemView
      • Load a trajectory
      • Energy vs. frame
      • Geometric properties with colour mapping
      • Atom-level forces
      • Cell parameters
      • Single structure
      • Custom property names
      • Periodic bulk structure
    • Interactive Demo
    • Requirements
    • Parameters
    • Property Specs
      • Structure-level
      • Atom-level
    • Auto-generated Names
    • Constraints
    • API Reference
      • ChemView()

Reference

  • API Reference
    • Core Modules
    • Utilities
  • Contributing
    • Development Installation
    • Code Style
    • Building the Documentation
    • Running the Tests
    • Pull Request Checklist
    • Reporting Issues
SPARC
  • User Guide
  • View page source

User Guide

Concept-level documentation explaining how SPARC works and how to configure it for your system.

  • Workflow Overview
    • Stage 1 — DFT / AIMD (00.dft)
    • Stage 2 — MLIP Training (01.train)
    • Stage 3 — ML-MD + Query-by-Committee (02.dpmd)
    • How sections in input.yaml map to stages
    • Loop termination
  • Active Learning
    • Force deviation thresholds
    • Number of models
    • Restarting an interrupted run
    • Restart exploration
  • Choosing a DFT Engine
    • VASP
    • CP2K
    • ORCA
    • xTB
    • Quantum ESPRESSO (QE)
    • Gaussian
  • Fine-Tuning vs. Training From Scratch
    • Training from scratch (default)
    • Fine-tuning a DeePMD universal model
    • When to use fine-tuning
  • Enhanced Sampling with PLUMED
    • Any PLUMED collective variable or bias can be used
    • Umbrella sampling
Previous Next

© Copyright 2024.

Built with Sphinx using a theme provided by Read the Docs.