ML-FSM Documentation

ML-FSM is a Python implementation of the Freezing String Method (FSM) for automated transition state (TS) guess generation. It supports multiple interpolation strategies including redundant internal coordinates (RIC), linear synchronous transit (LST), and Cartesian interpolation, and is designed to work with any ASE-compatible calculator, including machine learning interatomic potentials.

Key features:

• RIC interpolation — produces chemically realistic intermediate geometries and enables larger interpolation step sizes with fewer optimization steps per cycle

• Calculator agnostic — works with ML potentials (AIMNet2, MACE-OFF, TensorNet, xTB, FAIR UMA) and quantum chemistry codes (Q-Chem via file I/O)

• Fixed-atom support — freeze a subset of atoms during interpolation and optimization

• Flexible step-size control — specify node count or an explicit Cartesian step size

• Open source — MIT-licensed, installable from PyPI

Contents:

• Installation
  • Requirements
  • Install from PyPI
  • Install from Source
  • Optional: Calculator Dependencies
• Examples
  • Quick Start — EMT Calculator
  • Using Fixed Atoms
  • Specifying a Step Size Directly
  • Choosing an Interpolation Method
  • Using an ML Calculator (UMA Small 1.2)
  • Google Colab Tutorial
  • Example Reactions
• API Reference
  • Core Modules

Indices and tables

• Index

• Module Index

• Search Page