flare
stable
Installation
Tutorials
Introduction to FLARE: Fast Learning of Atomistic Rare Events
Prepare your data
Training a Gaussian Process from an AIMD Run
On-the-fly training using ASE
After Training
Code Documentation
Frequently Asked Questions
Applications
How To Contribute
How to Cite
flare
Docs
»
Tutorials
Edit on GitHub
Tutorials
ΒΆ
Introduction to FLARE: Fast Learning of Atomistic Rare Events
Installation
Machine learned force fields
Training data
Training a GP model.
Task: Find hyperparameters that give a positive log marginal likelihood.
Solution
Optimizing the hyperparameters rigorously
Calculating the learning curve
Learning a force field on the fly
The Lennard Jones Potential
Perform an on-the-fly training simulation.
Step 1: Set up the initial structure.
Step 2: Set up a GP model.
Step 3: Set up an OTF training object.
Step 4: Perform the simulation!
Parsing the OTF output file.
Comparing the learned model against ground truth.
Prepare your data
VASP data
Data from Quantum Espresso, LAMMPS, etc.
Try building GP from data
Training a Gaussian Process from an AIMD Run
Roadmap Figure
Step 1: Setting up a Gaussian Process Object
Step 2 (Optional): Extracting the Frames from a previous AIMD Run
Step 3: Training your Gaussian Process
Pre-Training arguments
On-the-fly training using ASE
Step 1: Set up supercell with ASE
Step 2: Set up FLARE calculator
Optional
Step 3: Set up DFT calculator
Optional: alternatively, set up Quantum Espresso calculator
Step 4: Set up On-The-Fly MD engine
Step 5 (Optional): Resume Interrupted Training
After Training
Parse OTF log file
Construct GP model from log file
Map the GP force field & Dump LAMMPS coefficient file
Run LAMMPS with MGP pair style
Read the Docs
v: stable
Versions
latest
stable
jon-cpp
development
Downloads
On Read the Docs
Project Home
Builds
Free document hosting provided by
Read the Docs
.